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Location: Remote - India MariaDB is making a big impact on the world. Whether you’re checking your bank account, buying a coffee, shopping online, making a phone call, listening to music, taking out a loan or ordering takeout – MariaDB is the backbone of applications used everyday. Companies small and large, including 75% of the Fortune 500, run MariaDB, touching the lives of billions of people. With massive reach through Linux distributions, enterprise deployments and public clouds, MariaDB is uniquely positioned as the leading database for modern application development. About the Role We're looking for a Senior Data Analytics Engineer who lives and breathes data-someone who combines strong engineering skills with analytical depth. You'll join a small, high-impact team where you don't just analyze data, you build the systems and pipelines that make analytics possible. This isn't a dashboards-and-decks role-you'll engineer solutions across the full analytics stack, from designing reliable data pipelines in BigQuery and MariaDB to building internal tools and APIs that put insights into the hands of the business. Day to day, you'll write sophisticated SQL in BigQuery and MariaDB, architect and build automation and tooling in Python and FastAPI, and use AI-powered development tools like Windsurf, Gemini, and Claude as a core part of how you work. You need to understand data architecture deeply-how information flows from source systems through pipelines into the warehouse, how schemas are designed, and how to build infrastructure that scales and stays maintainable. Communication is a big part of this role. You'll work closely with stakeholders across the business, translating complex data into clear, actionable insights. But equally important is your ability to engineer robust, repeatable solutions-not one-off analyses. We need someone who can own their domain end-to-end: define the right questions, build the data infrastructure, deliver the analysis, and present it in a way that drives decisions. If you're self-sufficient, ambitious, always looking to improve, and you have a GitHub profile that shows what you're capable of-we want to talk to you. Key Responsibilities Design, build, and maintain analytical pipelines and reporting solutions on BigQuery and MariaDB Write sophisticated, performant SQL to extract insights from large-scale datasets Leverage AI and machine learning features to augment analytics workflows and deliver smarter outputs Use AI-powered development tools (Windsurf, Gemini, Claude, Cursor) to accelerate your workflow and deliver at a higher level Develop internal tooling and automation using Python, FastAPI, and modern frameworks Communicate findings clearly to technical and non-technical stakeholders-translate data into action Understand and document data architecture, lineage, and how information flows across systems Collaborate on business process analysis and process design improvements driven by data Proactively identify opportunities for deeper analysis, better tooling, and operational improvements Requirements & Qualifications 7+ years of professional experience in data analytics engineering, data engineering, or a related field Expert-level SQL - you think in joins, window functions, and CTEs; BigQuery and MariaDB experience strongly preferred Strong Python and/or JavaScript skills - comfortable building pipelines, internal tools, APIs, and automation; not just scripts js experience - ability to build data-facing web applications and internal dashboards Deep understanding of data architecture - how databases are structured, how data flows, how schemas evolve Hands-on experience with AI/ML - you stay current with the latest capabilities (LLMs, generative AI, embeddings) and know how to apply them practically Proficiency with AI-powered development tools - Windsurf, Gemini, Claude, Cursor, or similar; you use AI to multiply your output, not as a crutch Excellent communication skills - you can explain complex analysis to anyone in the room Self-sufficient and self-managing - you own your work end-to-end without needing to be told what to do next Ambitious and continuously improving - you seek out new skills, tools, and techniques on your own A public GitHub profile with examples of your work (personal projects, contributions, experiments) Nice to Have FastAPI experience for building internal APIs and services Background in business process design or operational analytics Exposure to multiple business departments (Finance, Marketing, Sales, Operations, Engineering) - you understand how data flows across an organization, not just within one silo Understanding of Docker and Kubernetes - containerization and cluster orchestration experience is a big plus Location – India (remote) Travel - As required for the job (minimal). What's in it for you? Impact the world of technology by pushing the boundaries of technology and business models, working at MariaDB, plc. Be part of a game-changing organization that encourages outside-the-box thinking, values empowerment, and is truly shaping the future of the software industry. You’ll be collaborating with high-caliber colleagues around the world, offering unparalleled learning and growth opportunities. We provide a very competitive compensation package, 25 days paid annual leave (plus holidays) and a massive degree of flexibility and freedom, and more. Salaries for candidates outside the U.S. will vary based on local compensation structures. How to Apply If you are interested in this position, please submit your application along with your CV/Resume through our ATS Greenhouse on our MariaDB Careers site. At MariaDB we celebrate the diverse experiences and perspectives of our employees because this drives innovation and success. MariaDB is an equal opportunity employer dedicated to creating a welcoming and inclusive workplace for everyone. MariaDB does not sponsor work visas or relocation. MariaDB is committed to providing accommodations for individuals with disabilities within our application and interview process. To request an accommodation due to a disability, please inform your recruiter. MariaDB will not accept agency resumes without a prior contractual agreement with HR. Please do not forward resumes to any recruiting alias or employee directly. MariaDB is not responsible for paying any fees associated with any unsolicited submitted CV/Resumes.

Location: Almaty, Kazakhstan Workplace: OnSite WHY WORK AT HIGGSFIELD AI? Higgsfield AI is the fastest-scaling generative AI company in history, hitting $1B in annual revenue run rate, 30M+ users worldwide, 6M+ generations per day, and powering 390 of Fortune 500 brands. We're building at the absolute frontier of AI-powered video creation and next-generation creative tools. Joining Higgsfield means becoming part of a high-impact team shaping the future of AI-native experiences, at a company that isn't just moving fast, but rewriting what fast looks like. WHAT THIS ROLE MEANS AT HIGGSFIELD Most subscription businesses lose a password. We lose GPU-seconds. When someone runs two hundred accounts off one card to harvest a per-account promotional grant, the cost lands on our compute bill the same hour, and it does not stop until somebody finds them. We have found operators doing exactly that — hundreds of accounts, synchronised signups, machine- generated emails, round-the-clock automation — and we have also found that most of our worst- margin accounts are not abusers at all, just heavy users on an unlimited plan. Telling those two apart, account by account, with evidence that survives being challenged, is this job. You are the person who finds them, proves it, and puts a number on it. You make sure: - Every enforcement action we take is backed by evidence a stranger could follow - We know what abuse cost us last month, on the same basis as last month - The detection system is measured against real cases, not against its own past decisions - When a ring changes shape, somebody notices this week — and that somebody is you WHAT YOU WILL DO Investigation & casework - Run ring investigations end to end: from a flagged account or a cost anomaly to a named cluster, its members, its signature, its lifetime economics and a recommended action. - Build the linkage yourself — shared payment instruments, IP and ASN concentration, email-stem families, registration bursts, behavioural and automation fingerprints — and know which links are evidence and which are coincidence. - Separate abuse from unprofitable-but-legitimate every single time. On an unlimited plan, negative gross margin is normal. Cost is a gate, never a trigger. - Triage the review queue the detection system produces, and feed what you learn back into it. Your adjudications are the only unbiased labels the scientist has. Evidence & defensibility - Produce evidence packages that hold up outside the company — to an external auditor, to a payment network, to a customer disputing a ban, to Legal enforcing our Terms. Our abuse work has already been used in all four contexts. - Write per-account, not per-ring, when the action is per-account. A ban list without a reason column is not evidence. - Know what our Terms of Use actually entitle us to do, and flag when the evidence supports the finding but not the action. Quantification & reporting - Own the abuse loss number: what it cost, on what basis, net of the revenue we reversed and the legitimate customers we caught by mistake. Same definition every month, written down. - Distinguish COGS burned, revenue at risk, revenue reversed and cash refunded — they are four different numbers and people will conflate them. - Build the recurring reporting that tells us whether the problem is growing, and the ad-hoc answer to "why did compute spike last Tuesday" that is due the same day. Monitoring & adaptation - Watch the detectors themselves. A rule that stops firing is a detection event, not a quiet week. - Check the instrument before explaining the movement: a missing upstream table, a silently dropped join, a vendor signal that went away. This has bitten us and it will again. - Spot new abuse patterns before they are in anybody's model — usually in a cost anomaly, a strange cohort, or a support ticket that does not fit. How we work - SQL and Python on raw payment, usage and identity data in BigQuery, without anyone preparing it for you. We use AI heavily — resourcefulness beats syntax. - Close partnership with the Antifraud data scientist (you validate what they build), Payments (chargebacks and processor signals), Support (the humans who meet your false positives) and Finance (the loss number goes in the accounts). WHO WE'RE LOOKING FOR - 2+ years doing investigative or risk analytics — fraud, abuse, trust and safety, AML, chargebacks, gaming or marketplace integrity. What matters is that you have chased real bad actors, not modelled them in the abstract. - Strong SQL, without help. Self-joins, window functions, cohort replay, and joining payments to usage to identity across messy keys. This is the single most-used skill in the role. - Python at working level for analysis — pandas, notebooks, a bit of graph work. - Investigative instinct: you follow the thread, you notice the thing that does not fit, and you can say when a pattern is a coincidence. - Evidentiary discipline: you can write up a finding so that somebody who distrusts you can check it, and you know the difference between "this looks like abuse" and "this is provable." - Unit-economics literacy: gross margin per account, what compute costs, and why a negative-margin customer may be entirely legitimate. - The judgment to escalate rather than act when the evidence is thin, and to say so in writing. - Clear written and spoken English, B2+. Backgrounds that often do well: - Fraud / risk analysts from fintech, payments, marketplaces, gaming, crypto or betting - Trust & safety investigators and platform-integrity analysts - Chargeback, dispute or AML analysts who write their own queries - Strong product or BI analysts who got handed the abuse problem and kept it WHAT THIS ROLE IS NOT This role is not a fit if you: - Want to build models full time — that is the Antifraud data scientist, and it is open - Want to do content moderation or NSFW classification - Would put an account on a ban list without being able to say why in one sentence - Treat every negative-margin account as an abuser - Need a labelled dataset or a clean table before you can start - Are uncomfortable that your work gets real people's accounts restricted, sometimes wrongly - Want predictable 9–5 workdays HIRING PROCESS - Screening call (30 min) - Hiring-manager screen (30 min) - Business case (1,5 hour) - Tech interview (1,5 hour) - Final interview (30 min) - Paid on-site trial (1 month) THE DEAL - Competitive salary in USD - Equity: participation in the company's stock option program - On-site role in Almaty, Kazakhstan - Relocation support (flight + temporary housing) - Flat structure, high autonomy, fast career growth

Location: Almaty, Kazakhstan Workplace: OnSite WHY WORK AT HIGGSFIELD AI? Higgsfield AI is the fastest-scaling generative AI company in history, hitting $1B in annual revenue run rate, 30M+ users worldwide, 6M+ generations per day, and powering 390 of Fortune 500 brands. We're building at the absolute frontier of AI-powered video creation and next-generation creative tools. Joining Higgsfield means becoming part of a high-impact team shaping the future of AI-native experiences, at a company that isn't just moving fast, but rewriting what fast looks like. WHAT THIS ROLE MEANS AT HIGGSFIELD Generative video has a problem most subscription businesses don't: every unit we sell costs us real compute. A stolen password costs a streaming service nothing. An account farm on an unlimited generation plan costs us GPU-seconds, at scale, every hour it runs. We have found organised operators running hundreds of accounts on a single card, reselling unlimited generations, and harvesting per-account promotional grants at industrial scale. We have also found that most of our negative-margin accounts are not abuse at all — they are ordinary heavy users on an unlimited plan, which is a pricing problem wearing a fraud costume. Telling those two populations apart, at account level, before the money is gone, is this job. You own the question of who is taking value they did not pay for, and the system that stops them. You make sure: - We catch abuse before the compute is burned, not in a post-mortem that reconciles the loss - Every enforcement action has a measured precision, a measured cost of being wrong, and a way back for the customer we got wrong - The difference between fraud and unprofitable pricing is a number we can defend, not a vibe - When operators adapt — and they will, within days — we find out from a monitor, not from the monthly margin review WHAT YOU WILL DO Detection & scoring - Own the account-abuse scoring system end to end: signals, weights, thresholds, the scoring job, the monitoring, and the recalibration when it decays. - Build detection that fires early. A rule that catches 88% of the money on day 11 is worth far less than one that catches 60% at checkout, and you should be able to say why in dollars. - Work the two halves that actually matter together: linkage (who is connected to whom) and economics (what are they consuming). Either one alone produces a system nobody can switch on — linkage without economics flags families sharing a card, economics without linkage flags our best customers. - Treat cost as a gate, not a label. On an unlimited plan, being gross-margin negative is normal. Entity resolution & ring detection - Find rings, not just accounts: shared payment instruments, IP and ASN concentration, email-stem families, synchronised registration bursts, behavioural fingerprints, automation signatures. - Do it without a reliable device fingerprint — we do not have one today, and part of this job is telling us what it would be worth and what it would cost. - Know where graph methods break. Transitive closure through a shared card will happily merge thousands of unrelated people into one "ring"; we have done exactly that and thrown the result away. Enforcement, policy & the cost of being wrong - Design graduated, reversible actions — throttle, rate-limit, step-up verification, hold, pre-grant refusal, manual review queue, ban — and match the severity to the confidence. - Own the false-positive budget explicitly: how many paying customers are we willing to inconvenience to save a dollar of compute, and what is the appeal path for the ones we get wrong. - Build the pre-grant gates. The cheapest abuse to stop is the kind we decline to enable in the first place — a trial we do not grant costs nothing to claw back. - Partner with Payments on Stripe Radar, chargebacks and dispute economics; with Support on the queue your model creates; with Legal on what our Terms actually entitle us to do. Measurement & adversarial monitoring - Quantify what the system is worth in money — COGS avoided, net of the revenue you destroyed and the support cost you created. Keep that number current. - Run holdouts even on enforcement, wherever it is legal and safe to do so, because otherwise you will never know what your rules are worth. - Monitor for adaptation, not just drift. Model decay here is caused by an adversary, and it looks different: a signal stops firing, a ring changes shape, a cohort's behaviour shifts inside a week. - Watch your own inputs. A detection system that silently loses a feature degrades to a system that cannot reach its own threshold — and reports nothing wrong while doing it. Making it usable - Ship scores and reasons, never scores alone. An analyst, a support agent or an auditor must be able to read why an account was actioned. - Produce evidence that survives outside the company. Our abuse work has already been used in an external audit and in Terms-of-Use enforcement; write for that standard. - Work in SQL, Python and BigQuery on raw event, payment and generation data. We use AI heavily — resourcefulness beats syntax. WHO WE'RE LOOKING FOR - Experience building abuse, fraud or trust-and-safety detection that actually took action on real users — payments fraud, promo abuse, multi-accounting, bonus abuse, account takeover, marketplace or gaming abuse. Anti-money-laundering and credit-risk backgrounds transfer if the modelling was yours. - Genuine understanding of the adversarial setting: that your counterparty responds, that your training labels are generated by your own past decisions, and that offline AUC on a censored label set is close to meaningless. - Strong SQL and Python on raw event data without help: joins across payments, usage and identity, window functions, cohort replay. Gradient boosting, and the judgment to know when a two-condition rule beats a model. - Comfort with graph and linkage thinking — connected components, shared-attribute clustering, and an instinct for when a link is evidence and when it is coincidence. - Precision-first evaluation instincts: precision at the operating point, cost-weighted error, and why you would take 20% recall at 90% precision over the reverse in an enforcement system. - Unit-economics literacy: gross margin per account, what compute actually costs us, why a negative-margin customer may be entirely legitimate. - The judgment to escalate rather than act when the evidence is thin, and to say so in writing. - Clear written and spoken English, B2+. Backgrounds that often do well: - Fraud / risk data scientists from fintech, payments, marketplaces, gaming or crypto - Trust & safety and platform-integrity teams who owned detection and enforcement policy - Growth or product data scientists who ended up owning promo-abuse because nobody else would - Analysts from payment processors, PSPs or chargeback/dispute operations who moved into modelling WHAT THIS ROLE IS NOT This role is not a fit if you: - Want to build content-moderation or NSFW classification — that is a different function here - Would ship a ban list without knowing its precision - Optimize AUC on a label set your own enforcement created, and call it validated - Need a labelled fraud dataset to exist before you can start - Treat every negative-margin account as an abuser - Are uncomfortable that being right 90% of the time means being wrong about real, paying, legitimate people — and that you own what happens to them - Want predictable 9–5 workdays HIRING PROCESS - Screening call (30 min) - Hiring-manager screen (30 min) - Business case (1 hour) - Take-home with real-shaped data (7 days, ~10–12 hours of work) - Team interview (60 min) - Paid on-site trial (1 month) THE DEAL - Competitive salary in USD - Equity: participation in the company's stock option program - On-site role in Almaty, Kazakhstan - Relocation support (flight + temporary housing) - Flat structure, high autonomy, fast career growth

Location: Almaty, Kazakhstan Workplace: OnSite WHY WORK AT HIGGSFIELD AI? Higgsfield AI is the fastest-scaling generative AI company in history, hitting $1B in annual revenue run rate, 30M+ users worldwide, 6M+ generations per day, and powering 390 of Fortune 500 brands. We're building at the absolute frontier of AI-powered video creation and next-generation creative tools. Joining Higgsfield means becoming part of a high-impact team shaping the future of AI-native experiences, at a company that isn't just moving fast, but rewriting what fast looks like. WHAT THIS ROLE MEANS AT HIGGSFIELD We sell subscriptions and credit packs to consumers in over a hundred countries, and today almost all of it moves as cross-border card payments in USD. That has three consequences, and this role owns all three. We are declined more than we should be. In some of our largest growth markets roughly one attempt in three fails, and a meaningful share of those are not "no money" — they are cross-border blocks, card limits, stale credentials on renewal, and issuers who do not recognise us. We pay more than we should. Our blended cost of acceptance is the single largest line below compute, and the majority of the network component sits on transactions our processor classes as cross-border. We are not on the rails our customers use. In two of our fastest-growing markets, the dominant local payment method is one we do not offer at all. You own the measurement and the argument behind fixing all of it. You make sure: - We know our true authorization rate — at the unit the business actually cares about, not the one that is easy to compute - Every basis point of payment cost has a name, a geography and an owner - A decline is a diagnosis, not a statistic - When we propose a new rail, a new entity or a new processor arrangement, the number underneath it holds up to Stripe's own analysis and to our board's WHAT YOU WILL DO Authorization & decline analysis - Own authorization rate end to end: by country, issuer, BIN, card brand and funding type, plan price point, first payment vs renewal, and payment method. - Build and maintain the decline taxonomy — what each decline code actually means, which are recoverable, which are terminal, and which are us rather than them. - Measure at the right unit. An invoice that succeeds on the third retry is a success, not two failures and a success, and reporting it the other way has made teams optimise the wrong thing. - Separate soft declines we can recover from hard declines we cannot, and put a number on the recoverable pool. Cost of acceptance - Own the blended take rate and its decomposition: interchange, scheme fees, processor markup, cross-border and currency-conversion components, disputes, and the fixed per-transaction pieces. - Find where the cost actually sits. Averages hide it — the cost is concentrated by corridor, by card type and by whether the transaction is domestic. - Build the evidence for commercial conversations with our processor, and keep it current enough to reuse. Local payment methods, entities and routing - Size the case for new rails market by market: what share of local commerce runs on them, what our current card performance costs us, and what we would realistically recover. - Model merchant-of-record and local-acquiring arrangements against the alternative of our own entities, honestly — including the fee we would pay for the privilege. - Design and read the experiments that prove it, with the product analytics team. A new payment method is an A/B test, not a launch. Renewals, dunning and involuntary churn - Own the retry and dunning strategy as a measurable system: when to retry, how often, through which rail, and when to stop. - Quantify involuntary churn separately from voluntary. They look identical in a cancellation count and they need completely different fixes. - Track the health of stored credentials — network tokens, card-account-updater coverage, expiring cards — because in a subscription business that is silent revenue leakage. Disputes, refunds and reconciliation - Own dispute and chargeback rates against network thresholds, with the lag handled properly — disputes arrive weeks after the charge and a same-period denominator understates you. - Reconcile processor data to the revenue ledger and explain the differences rather than plugging them. - Work with the antifraud team where disputes are abuse rather than genuine dissatisfaction. HOW WE WORK - SQL and Python on raw processor and billing data in BigQuery. We use AI heavily — resourcefulness beats syntax. - Partner with Finance (the numbers get booked), Product (checkout and paywall), Engineering (the billing integration) and our processor's team directly. WHO WE'RE LOOKING FOR - 2+ years in payments analytics, payment operations, billing, or risk at a merchant, PSP, acquirer, or fintech. This is a domain role — general analytics experience without payments does not transfer quickly enough. - Fluency in the actual mechanics: authorization vs capture vs settlement, decline codes, 3DS and SCA, network tokens and account updater, interchange and scheme fees, chargeback lifecycle, MCC, BIN, cross-border and DCC. - Strong SQL, without help. Most of this job is joining sessions to intents to charges to invoices to refunds across imperfect keys and getting the denominator right. - Python at working level for analysis. - The instinct to check the unit of measurement before quoting a rate — per attempt, per invoice, per user, per session are four different numbers and only one of them answers the question. - Comfort with subscription billing specifically: renewals, proration, plan changes, failed payments, dunning, involuntary churn. - Commercial judgment: you can build the case for a rail or a rate and defend it against a vendor who does this for a living. - Clear written and spoken English, B2+. Backgrounds that often do well: - Payment operations or payments analytics at a consumer subscription, marketplace or gaming company - Analysts from PSPs, acquirers, orchestration platforms or card networks - Billing / revenue operations analysts who owned dunning and involuntary churn - Risk or chargeback analysts who moved toward the acceptance side WHAT THIS ROLE IS NOT This role is not a fit if you: - Want to own checkout UX or pricing — that is Product Analyst, and it is open - Want to build fraud models — that is the Antifraud data scientist, and it is open - Would report an authorization rate without saying what the denominator was - Would take a processor's own analysis of their own performance at face value - Need someone else to prepare the data - Want predictable 9–5 workdays HIRING PROCESS - Screening call (30 min) - Hiring-manager screen (30 min) - Business case (1 hour) - Take-home with real-shaped data (7 days, ~10–12 hours of work) - Team interview (60 min) - Paid on-site trial (1 month) THE DEAL - Competitive salary in USD - Equity: participation in the company's stock option program - On-site role in Almaty, Kazakhstan - Relocation support (flight + temporary housing) - Flat structure, high autonomy, fast career growth

Задачи: — Установление и поддержка инфраструктурных паттернов для рабочих нагрузок данных. — Проектирование и поддержка эталонных паттернов для платформ данных ZoomInfo: стриминг и месседжинг (Kafka/Confluent, Pub/Sub), операционные базы данных (PostgreSQL/Cloud SQL, MongoDB), аналитические хранилища (BigQuery, Snowflake), поиск (Elasticsearch/Solr), оркестрация и обработка пайплайнов (Apache Airflow/Cloud Composer, Dataflow/Apache Beam), открытые форматы таблиц (Apache Iceberg). — Разработка Terraform-модулей, GitOps-воркфлоу и документированных стандартов для использования командами данных. — Определение CI/CD и GitOps паттернов для задач и пайплайнов данных, сотрудничество с платформенной и дата-инженерной командами для безопасной и автоматизированной доставки данных. — Установление паттернов производительности, стоимости и наблюдаемости, включая оптимизацию запросов и хранения, мониторинг, алерты и практики SLO. — Определение базовых паттернов для резервного копирования, восстановления и аварийного восстановления данных с целями RTO/RPO. — Сотрудничество с FinOps по расходам на уровне данных. — Проведение обзоров рабочих нагрузок команд на соответствие паттернам и помощь в выравнивании. — Проведение POC новых технологий данных и предложений поставщиков в рамках процесса закупок и жизненного цикла технологий. — Определение паттернов подключения управляемых поставщиками сервисов данных к GCP с учетом безопасности, сетей и контроля затрат. — Поддержка усилий по консолидации данных и управлению ими, включая классификацию, резидентство и политику доступа. — Создание эталонных реализаций, написание ADR и сотрудничество с командами для внедрения паттернов. — Участие в еженедельных архитектурных обзорах, расширение библиотеки стандартов и каталога ADR. — Проведение дизайн-ревью, воркшопов и консультирование команд по инфраструктурным вопросам. Требования: — Умение влиять и коммуницировать, представлять рекомендации с прототипами, бенчмарками и обоснованиями. — Опыт архитектуры облачной инфраструктуры на GCP и/или AWS, включая compute, Kubernetes (GKE или эквивалент), VPC, IAM, стоимость и надежность. — Сильные навыки Terraform и GitOps, включая проектирование и ревью модулей. — Умение с уверенностью оценивать дизайн сервис-меш и сетей. — Практический опыт работы с инфраструктурой данных в масштабе от нескольких терабайт до петабайт с высокими нагрузками и чувствительностью к задержкам. — Глубокий опыт в минимум двух областях данных: стриминг и обработка потоков (Kafka/Confluent, Flink), реляционные операционные базы (PostgreSQL), NoSQL (MongoDB), колоночная аналитика и lakehouse (BigQuery, Snowflake, Iceberg), поиск (Elasticsearch/Solr), оркестрация и обработка пайплайнов (Airflow, Dataflow/Beam). — Навыки оптимизации производительности и стоимости, диагностики и исправления медленных или дорогих рабочих нагрузок. — Способность разрабатывать и тестировать прототипы, предпочитая рабочие решения диаграммам. — Опыт проведения технических оценок технологий и подготовки рекомендаций для руководства и закупок. Преимущества: — Опыт поддержки миграции lakehouse или data mesh с инфраструктурной стороны. — Реализация контроля классификации данных, резидентства и соответствия GDPR/CCPA на уровне инфраструктуры. — Опыт эксплуатации графовых или распределённых SQL баз данных. — Знание инфраструктурных требований AI/ML рабочих нагрузок. — Опыт создания или применения AI-усиленных рабочих процессов для разработчиков или архитекторов. — Участие в архитектурных советах или аналогичных органах управления. Образование и опыт: — Степень бакалавра в области компьютерных наук, технической области или эквивалентный практический опыт. — 10–15+ лет опыта в облачной инфраструктуре, платформенной или дата-инфраструктурной инженерии, включая несколько лет установления паттернов и стандартов, от которых зависят несколько команд. Условия: — Удалённая работа. — Годовая зарплата от $157,500 до $247,500 USD. — Дополнительная компенсация, включая бонусы, комиссионные, акции и другие льготы.

Location: Bangalore Workplace: Hybrid About the role We’re looking for an experienced Senior Backend Software Engineer with 4+ years of experience to help build and evolve this product. Strong hands-on experience with Go (Golang) is a must. You’ll be an early contributor to a new and rapidly evolving product area, working on complex backend and security challenges at significant scale while having meaningful input into the architecture. What you’ll do - Design, extend, and optimize large-scale, highly scalable, fault-tolerant backend systems processing billions of events from hundreds of thousands of endpoints in real time, with sub-second latency requirements. - Build security capabilities that protect sensitive customer data as it flows into and out of LLMs, GenAI applications, AI tools, and agents. - Contribute to the architecture and evolution of Cyberhaven’s AI Security platform. - Solve real-world scaling and distributed systems problems through performance analysis, profiling, optimization, and troubleshooting. - Build and ship production backend services using Go (Golang). - Work with a modern microservices-based stack including Go, Kubernetes, BigTable, BigQuery, Spanner, Redis, Docker, and Etcd. - Build integrations and connectors at the API level with SaaS and AI platforms commonly used in enterprises, including GenAI and LLM-based applications. - Write secure-by-design, hardened software that can withstand real-world attacks while processing untrusted data and communicating with hundreds of thousands of endpoints over the internet. - Collaborate closely with engineers, product teams, and distributed teams across India, Europe, and the US. Who you are - You have 4+ years of software engineering experience, with a strong track record of building backend systems that are actively used at scale. - You have strong hands-on experience with Go (Golang) and have built and shipped production backend services using Go. - You have experience building and operating distributed systems, microservices, and Kubernetes-based applications in production. - You have worked with databases at significant scale. Experience with GCP services such as BigTable, BigQuery, or Spanner is a plus. - You have experience profiling, troubleshooting, and optimizing distributed applications that use large databases, event streams, or streaming platforms. - Experience building API-level integrations or connectors with enterprise SaaS applications is a plus. Experience with AI/LLM platforms is especially relevant. - You understand the importance of reliability, performance, and security when building systems that process large volumes of sensitive or untrusted data. - You have excellent verbal and written communication skills in English and are comfortable collaborating frequently with distributed teams in Europe and the US. - You thrive in fast-paced environments, enjoy tackling ambiguous technical challenges, and like shipping with velocity. - You have a customer-centric mindset and value direct feedback from customers. - You are based in Bangalore. Joining Cyberhaven means joining the team building Data Security for the Agentic Enterprise. Traditional tools fall short. Cyberhaven traces the full lifecycle of your data, adapting protection to changing context, so protection moves with the way people actually work, not against it. AI Changed Work. We Protect It. Backed by $250M from leading investors like Khosla and Redpoint, our team includes leaders who built industry-defining technologies at CrowdStrike, Palo Alto Networks, Meta, Google, and more. This role lets you shape the future of data security, working alongside people driven to protect workflows, not just data, for customers who cannot afford to slow down to stay safe. AI Tools Disclosure: As part of Cyberhaven's hiring process, we use recruiting tools that include AI-powered features to help with tasks like scheduling, note-taking, workflow automation, and other recruiting activities ("AI Tools"). The AI Tools may process information you provide during the application and interview process. When AI-assisted interview note taking is used, candidates are notified in advance and have the option to opt out of the AI-assisted interview note taking. Cyberhaven is committed to creating a diverse environment and is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.

Location: Remote - India AI/ML Solution Architect Education & Assessment Platforms About Pratham International Pratham International is a US 501(c)(3) established to support learning innovations for children and youth in contexts across the globe. We enable the adaptation and spread of proven and scalable education solutions pioneered in India by Pratham Education Foundation to contexts across the global south through programming and technical assistance tailored to local realities. Today, Pratham innovations reach millions of children annually through partnerships across ~30 countries. Through the Pratham Shah PraDigi Innovation Centre (PraDigi), Pratham International also seeks to create technology innovations for low-resource communities, and support models aimed at learning for school, work, and life for children and youth. We work on several cutting-edge projects in the domain of Generative AI, Automatic Speech Recognition, and Natural Language Processing. The team’s innovative technology products include the PadhAI: Reading Assessment App, which uses advanced speech recognition to evaluate children’s reading fluency in 8 Indian languages and 6 global languages; Anytime Testing Machine (ATM), an AI-enabled question generation and grading system that allows learners to be assessed on any topic, anytime; and a suite of AI-powered chatbots designed for diverse learning contexts: from early childhood care, to youth employment readiness, and OCR-based analysis tools that digitize and assess student work at scale. At Pratham International, we build for the long term. Whether it’s ensuring products are truly accessible across rural India and the global south through languages, building datasets to ensure context and accuracy for products, or gathering feedback from the field. Backed by partners like Google.org, Anthropic, and the Gates Foundation, we are committed to building digital public goods that ensure every child is in school and learning well. Role Overview About the work We design and deliver AI systems that governments and large education programmes can actually run: curriculum-aligned item generation, paper assembly, handwriting OCR, rubric grading, and teacher-in-the-loop review. The work is already live as practice assessment in India and is being built as a national exam platform in Rwanda, with further country programmes behind it. We are hiring a Solution Architect who sits between discovery and delivery. You will turn messy workshop notes and ministry requirements into architecture a team can build, secure, cost, and operate — RAG and multi-agent systems, LLM-as-judge pipelines, and the cloud/LLMOps layer underneath. You will Own solution design from first workshop to a delivery-ready blueprint (services, data stores, trust boundaries, cost, ops). Architect LLM pipelines: generation, retrieval over curriculum knowledge graphs, standards/duplication/quality judges, constraint-based paper assembly. Design human + AI workflows with audit, segregation of duties, cycle-pinned config, and no student PII in model calls. Choose and govern models, prompts, eval gates, and fallbacks (including OCR: Azure primary, secondary fallback). Shape the Azure/AWS platform: identity, networking, LLMOps/MLOps, observability, and sovereign-hosting constraints. Run client workshops, write POVs and architecture packs, and stay accountable until the system is in production. About the work Pratham International programmes sit at the intersection of pedagogy, public systems, and production AI. The pattern is consistent across countries; the constraints change. Anytime Testing Machine (ATM) — India, practice and feedback End-to-end assessment for real classroom conditions: generate curriculum-aligned items, digitise handwritten answers (often photographed on a phone), grade against rubrics, and return feedback a teacher reviews before it reaches the learner. Built for high pupil–teacher ratios, mixed language (e.g. Hindi + English), and uneven connectivity. Pilots have already reached thousands of learners, including Second Chance (women returning for Grade 10). Model quality is measured against expert golden sets, not vendor slides. National assessment automation A government-grade exam platform. Phase 1 is item authoring and print-ready paper composition before the exam window. Phase 2 is script intake, OCR at national volume, AI-assisted grading with human confirmation on the pilot, results export, and monitoring/evaluation reporting. Subjects span core and elective streams across the curriculum. Design rules that do not exist in a typical SaaS product: cycle-pinned configuration, embargoed papers, hash-chained audit trails, and integration with national student information and assessment management systems. Curriculum knowledge graphs Structured retrieval corpora (concepts + worked examples) that ground generation and judging. This is the standing public-knowledge exception in an otherwise closed exam system, and the seed of a broader education DPI conversation. AI in TaRL and teacher support AI tools that help teachers group and teach by learning level, with an RCT-shaped evidence bar. Same product family: human agency stays in the loop; the model is a capacity multiplier. Country programmes and the three-year direction Adapt the same engine to new ministries, languages, and exam rules (Kenya and other Global South programmes are in scope). The longer bet is a learning and credentialing engine that recognises competence beyond a single syllabus. This is not a chatbot brief. It is constrained generation, retrieval, judging, document AI, workflow, and public-sector operations. Why this role exists Most enterprise AI dies between the workshop and production. We need one person who owns that gap: someone who can sit with NESA, C4IR, Pratham pedagogy, and engineering in the same week, and leave behind a blueprint the delivery team can execute without inventing policy. The target profile is a practising AI/ML Solution Architect — not a pre-sales slide owner, and not an infra-only platform engineer. The person should have done all three of these: Designed GenAI / NLP solutions, PoCs, and client workshops, and can talk to research-grade issues (eval, agent safety, prompt-injection, model-specific behaviour). Owned the AI platform: LLMOps, MLOps, DevSecOps, SRE habits, cloud migration, and FinOps. Taken messy discovery and turned it into Azure-centred RAG and multi-agent designs that a security team will accept. Key Responsibilities Discovery → architecture Lead solution workshops with programme, ministry, and engineering stakeholders. Convert half-formed requirements into service cuts, data stores, sequence diagrams, decision gates, and open-question lists with owners. Write architecture decision records. When the docs disagree (they will), force a decision instead of coding both. AI system design Design LLM jobs as contracts: inputs, JSON schema, temperature, persist prompt hash / model / tokens / latency. Architect generation grounded in structured curriculum blueprints and knowledge-graph retrieval, not "ask the model for a maths paper." Design LLM-as-judge banks (cognitive-level, relevance, difficulty, language, fairness, standards, duplication, blueprint compliance) with thresholds, one-repair rules, and golden-dataset release gates. Design hybrid assembly: constraint solver first, model only on ties and layout exceptions. Design document AI: primary OCR provider, secondary fallback provider, confidence routing, de-identification before any grading call. Specify eval: inter-rater agreement, rubric-band exact match, first-pass item acceptance, cost per paper cycle. Platform, trust, cost Own the reference architecture on the chosen cloud platform(s): identity, RBAC + segregation of duties, private networking, key management, object storage for scans, vector index, data warehouse for monitoring/evaluation reporting. Draw trust boundaries. Student PII never crosses the model gateway. Prompts are curriculum + knowledge-graph + blueprint content only. Put FinOps on the design: token budgets, OCR cost caps, degradation behaviour when a cap is hit, model routing. Specify LLMOps/MLOps: prompt/version registry, eval regression as a release gate, observability, rollback. Design for sovereign hosting and exam-cycle operations (embargo, watermarking, append-only audit, cycle-pinned configuration). Delivery and thought leadership Stay with the build through the first production cycle. Architecture that cannot be implemented is unfinished work. Produce POVs, sequence packs, and workshop artefacts that can be reused in the next country. Mentor tech leads on where the model should stop and the workflow should start. Success in the first two quarters First 30 days — Orient Complete architecture walkthroughs of every live/in-flight system with the current engineering leads; document current state (services, data stores, trust boundaries) as-is, not as-designed Meet every active stakeholder group once: engineering, programme/domain leads, and any external partner or government-side counterpart Inventory all open technical decisions and disagreements in existing specs; log each with current owner (if any) and status Identify the single highest-risk unresolved design question and flag it upward Day 60 — Diagnose Produce a current-state architecture diagram covering every major pipeline: data flow, model/LLM touchpoints, storage, and identity/access boundaries Assign an owner and target resolution date to every open contradiction identified in month 1 Draft a v1 reference architecture for one representative end-to-end workflow: service breakdown, data stores, identity/access model, model gateway placement, and a first-pass cost estimate Day 90 — Commit v1 reference architecture is reviewed and accepted by engineering and relevant stakeholders (not just self-signed) Can walk any core pipeline end-to-end from memory and name every data store, every quality/eval gate, and every trust boundary in it At least one architecture decision record is published for a previously-contested design question, with the decision forced rather than left open A first cost model exists for the reference architecture, with at least one identified lever for reducing spend if usage scales Day 120 — Harden Core pipeline components (generation, retrieval, judging/eval, or equivalent stages for this domain) have documented contracts: defined inputs/outputs, schema, and logged metadata (model, version, cost, latency) An evaluation harness exists for at least one component, with a defined pass/fail threshold A release gate exists that can block a bad prompt, model, or config change from reaching production Day 150 — Extend The next major system phase or capability is specified to the same rigor as the first: trust boundaries, failure modes, fallback behavior, and cost caps documented before build starts FinOps guardrails are in place for at least one production workload: budget ceilings, degradation behavior when a cap is hit, and a routing or fallback strategy Day 180 — Generalize A reusable design pattern or adaptation pack exists, separating what is fixed in the architecture from what varies by deployment context (e.g. language, hosting environment, external-system integration, content/domain rules) At least one engineering or technical lead has been mentored on where model-based logic should stop and deterministic/workflow logic should take over A second production-readiness review (security, cost, or operations) has been passed on a system this person designed or substantially redesigned Qualifications and Experience Must have 8+ years building software or data/AI systems, with recent experience as a Solution Architect, AI/ML Architect, or Principal/Staff Engineer with end-to-end architecture ownership. Shipped at least one production AI/ML system — RAG, multi-agent, LLM-as-judge, or a comparable classical ML/NLP pipeline at scale — that passed a real security, cost, and operations review, not just a pilot or a slide deck. Can produce a delivery-ready architecture blueprint: services, data stores, IAM, failure modes, evaluation strategy, and cost model — detailed enough that engineering isn't guessing at intent. Deep cloud experience on Azure and/or AWS: comfortable defending a landing-zone design, private-endpoint architecture, and a real monthly compute/inference/OCR spend. Fluent in Python, with hands-on experience in both classical ML (model training, evaluation, feature pipelines) and how LLM APIs, embeddings, and vector search actually behave under production load. Comfortable running a room with non-engineers — domain specialists, IT stakeholders, monitoring/evaluation teams — and translating fluently in both directions. Strong written English; this role's output is documentation that functions as a binding technical contract. Strongly preferred Azure AI (AI Foundry / OpenAI / Document Intelligence), plus one of AWS (Bedrock, SageMaker) or GCP (Vertex, BigQuery). LLMOps / MLOps: prompt + model versioning, MLflow or equivalent, eval harnesses, CI that can block a bad prompt. Document AI / OCR at volume; handwriting and low-quality scans, not only clean PDFs. RAG over structured knowledge (graphs, ToS, textbooks) and hybrid search, not only naive chunking. Education, assessment, public sector, or other high-audit domains (health, finance, identity). Certifications: Azure Data Scientist / Azure Solutions Architect; AWS SAA, ML Specialty, or Data Analytics Specialty. Experience with agent security: tool-use limits, DLP on egress, prompt-injection evaluation that is model-specific. FinOps discipline. You have killed a design because it would not survive the token bill. Nice to have Microsoft Fabric, Databricks, Semantic Kernel / Microsoft Agent Framework, MCP (Model Context Protocol) tool integration for agentic workflows. Constraint solvers or exam-blueprint / item-bank systems for automated test assembly. Multilingual generation and feedback, including code-mixed language pairs (e.g. Hindi–English, Spanish–English, or similar). Experience standing up golden/eval datasets with subject-matter experts. Prior work with government data systems, data-sharing agreements, or in-country/sovereign hosting requirements. What this role is not Not a research-only post. Publications help; shipping is the job. Not a pure platform/SRE hire. Infra is part of the design, not the whole design. Not a prompt engineer who hands off “the architecture” to someone else. Not a people manager of a large team on day one — you influence through artefacts and reviews. How we will assess you We value transparency and respect your time. Our hiring process is designed to evaluate both hands-on technical depth and architectural judgment through practical, real-world tasks. Stage 1: Application & Knockout Screening Submit your CV alongside key screening responses Artefact — send two of: architecture note, eval design, runbook, ADR. Slides without decisions do not count. Stage 2: Initial Conversation (30 mins) An introductory screen covering your background, project context and baseline alignment. An opportunity for you to ask questions about our technical roadmap, partner ecosystem, and vision. Stage 3: Practical Take-Home Written Assessment (2–3 hours) Design exercise on a constrained generation + judge + human-review pipeline (we will use a sanitised exam-authoring brief). We want trust boundaries, eval, and cost, not a model-name dump. Stage 4: Deep-Dive Technical & System Architecture Walkthrough Walkthrough of a system you shipped: what failed in review, what you changed, what it costs to run. Stage 5: Final Stakeholder & Vision Alignment Final conversation with key technical leadership and program stakeholders. Employment Details Function: Solutions / Architecture Reports to: Head of Technology / CTO Expected Start Date: Immediate Location: India (remote-first; travel to client workshops as needed) Experience: 8–13 years (strong 6–8 considered if you have shipped production GenAI) Engagement: Full-time Remuneration: Based on candidate’s experience and skillset Flexibility to work across timezones: Our team spans Latin America to Southeast Asia. Early mornings  or late evenings can be a regular part of the rhythm Why join us? Impact at Scale: Your work will directly support the learning journeys of millions of children. With programs active in 31 countries, you will help build Digital Public Goods that bridge the gap for the millions of children across the Global South who still struggle with foundational literacy Innovation at the intersection of Education and Technology: Lead the charge in redefining learning and learning processes through technology. You will contribute to the design and development of cutting-edge learning tools and AI-driven initiatives, ensuring EdTech isn’t just a buzzword, but a scalable solution for global equity. A Culture of Growth and Evidence: At Pratham, we value rigorous testing and continuous learning. You will have the autonomy to experiment with the latest SOTA models, supported by a 5-year strategic roadmap and funding from partners like the Gates Foundation and Anthropic. High-Calibre, Mission-Driven Team: You will work alongside a global team of experts from institutions like MIT, Harvard, and INSEAD. Our alumni have gone on to lead successful startups and international policy shifts, all while remaining dedicated to the goal of “Every Child in School and Learning Well.” Inclusive and Collaborative Environment: Join a diverse team that spans Asia, Africa, and Latin America. We value innovative thinking and local expertise, ensuring that the technology we build is deeply rooted in the realities of the communities we serve. Pratham International is an equal opportunity employer and encourages all qualified people from diverse backgrounds to apply for positions within our organisation, regardless of race, gender, disability or any other status.

Задачи: — Проектировать, строить и развивать агрегатную систему, которая определяет, управляет и оптимизирует агрегаты для ускорения производительности запросов на различных платформах данных. — Архитектурно оптимизировать логику предсказания и выбора, которая выявляет ценные агрегаты и анализирует рабочие нагрузки запросов для управления стратегиями размещения. — Улучшать процессы обслуживания и планирования, чтобы обеспечить надежность и низкие накладные расходы в масштабных средах, гарантируя плавное масштабирование системы. Требования: — Опыт профессиональной разработки ПО не менее 6 лет и степень бакалавра в области компьютерных наук или смежной области. — Сильные навыки программирования на Scala, желательно опыт работы с системами Scala worker, такими как Akka или Pekko. — Умение анализировать сложное поведение системы, состояние жизненного цикла и компромиссы производительности с акцентом на надежность в продакшене. — Опыт работы с концепциями хранилищ данных (Snowflake, BigQuery и др.) и технологиями контейнеризации (Docker, Kubernetes). — Отличные коммуникативные навыки и комфортная работа с командами планирования запросов и платформ. — Готовность работать в часовом поясе Восточной/Центральной Европы. Условия: — Гибкий формат работы: удалённо, в офисе или гибрид. — Конкурентная зарплата и хороший компенсационный пакет. — Персонализированный карьерный рост. — Инструменты профессионального развития (менторская программа, технические доклады и тренинги, центры компетенций и др.). — Активные технические сообщества с регулярным обменом знаниями. — Возмещение расходов на образование. — Памятные подарки к юбилеям. — Корпоративные мероприятия и тимбилдинги. — Другие льготы, зависящие от локации. Примечание: условия не применимы к фрилансерам.

Задачи: — Регулярная отчетность по продажам, ассортименту, запасам, промо и ценам — Построение витрин данных для товарных направлений — Подготовка выводов и рекомендаций — Анализ эффективности акций: uplift, влияние на маржу, каннибализация — Анализ ассортимента: ABC/XYZ, локомотивы, балласт, недостающие SKU — Анализ запасов: дефициты, излишки, оборачиваемость, неликвид — Анализ цен: конкуренты, эластичность, влияние цены на продажи — Аналитика каналов продаж: маркетплейсы, офлайн, опт — Контроль качества данных: корректность SKU, категорий, цен справочников — Поддержка категорийных менеджеров и маркетинга данными и выводами — Настройка алертов по ключевым товарным метрикам — Участие в создании единой системы товарной аналитики холдинга Требования: — Опыт в товарной аналитике: продажи, ассортимент, запасы, промо, цены — Уверенная работа с SQL (ClickHouse / PostgreSQL / BigQuery) — Опыт построения отчетов и витрин (Power BI / Superset / Datalens / Tableau) — Опыт работы с 1С на уровне выгрузки данных и понимания структуры регистров — Понимание товарных метрик: оборачиваемость, маржинальность, out‑of‑stock, overstock, неликвид — Понимание unit‑экономики товаров: вклад SKU в прибыль, эффективность акций — Опыт работы с большими таблицами и сложными связями данных (SKU, категории, каналы) — Понимание принципов единой системы аналитики: справочники, легенда метрик, единые определения — Навык проверки качества данных: поиск ошибок, несостыковок, дубликатов, некорректных SKU — Базовые навыки статистики: сезонность, тренды, простые прогнозы — Аккуратность и внимательность к деталям Будет плюсом: — Опыт в e‑commerce, ритейле, FMCG или дистрибуции — Опыт работы с большим ассортиментом (1000+ SKU) — Опыт анализа промо‑акций и их эффективности — Навыки Data Science: прогнозирование спроса, uplift промо — Опыт внедрения единой легенды метрик Условия: — Официальное оформление по ТК РФ с первого рабочего дня — График работы: понедельник-пятница, 10:00 — 19:00, формат работы готовы обсуждать индивидуально — ДМС — Корпоративная скидка на продукцию компании — Современный и комфортный офис в районе м. Преображенская площадь (БЦ Прео 8, Преображенская пл., 8) — Корпоративы (выездные и в офисе), лиги игр, различные клубы по интересам

Задачи: — Настройка корректного трекинга событий: пиксели, метрики, UTM‑метки, пользовательские действия — Построение воронок: регистрация, авторизация, покупка, переходы, отвалы — Создание отчетности по конверсиям: по страницам, по каналам, по кампаниям — Анализ пути пользователя: источник > покупка > повторное действие — Отчетность по рекламным кампаниям: эффективность каждого креатива, источника, кампании — Настройка алертов по аномалиям в конверсиях и событиях — Регулярные исследования поведения пользователей: отвалы, конверсии, влияние изменений — Подготовка рекомендаций: что улучшить в UX, что влияет на конверсии, где узкие места — Коммуникация с маркетингом, продуктом, разработкой – единый трекинг требует синхронизации Требования: — Опыт веб‑аналитики: настройка событий, пикселей, трекинга, UTM‑меток, атрибуции — Опыт построения воронок: регистрация, авторизация, покупка, переходы между страницами, отвала пользователей — Опыт работы с системами веб‑аналитики: Яндекс Метрика, Google Analytics, Amplitude, AppMetrica, GTM — Уверенная работа с SQL (ClickHouse / PostgreSQL / BigQuery) – построение воронок, когорт, путей — Опыт построения витрин и дашбордов (Power BI / Superset / Datalens) — Понимание принципов корректного сбора данных: единые события, единые названия, единая легенда метрик — Опыт анализа рекламных кампаний: post‑click конверсии, эффективность источников, путь пользователя — Понимание продуктовых метрик: MAU/WAU/DAU, конверсии, ретеншен, LTV, CPA, ROMI — Опыт A/B‑тестирования: постановка, анализ, интерпретация результатов — Системность и структурность – умение выстраивать единый трекинг и единые правила Будет плюсом: — Опыт работы с API рекламных систем (Яндекс.Директ, VK Ads, Google Ads) — Опыт в e‑commerce, маркетплейсах, медийных проектах — Опыт построения сквозной аналитики и конструкторов воронок — Навыки Data Science и опыт работы с Amplitude / Mixpanel / AppMetrica — Опыт работы с большим трафиком и сложными пользовательскими путями Условия: — Официальное оформление по ТК РФ с первого рабочего дня — График работы: понедельник-пятница, 10:00 — 19:00, формат работы готовы обсуждать индивидуально — Сильную среду для роста - поддерживаем развитие, формируем доступ к актуальной профессиональной экспертизе — Заботу о здоровье - ДМС — Корпоративную скидку на продукцию компании — Офис - современный и комфортный офис в районе м. Преображенская площадь(БЦ Прео 8, Преображенская пл., 8) — Корпоративы (выездные и в офисе), лиги игр, различные клубы по интересам — Команду, с которой можно реализовывать по-настоящему масштабные задачи

Задачи: — Design, build, and maintain data infrastructure and pipelines spanning both batch and real-time / streaming workloads, contributing to architectural decisions along the way — Build and maintain scalable, efficient, and reliable ETL/ELT pipelines using languages and frameworks such as Python, SQL, Spark, Flink, Beam, or equivalents — Work on real-time or near-real-time data solutions (e.g. CDC, streaming, micro-batch) for use cases that require timely data — Partner with data scientists, ML engineers, analysts, and product teams to understand data requirements, define SLAs, and deliver coherent data products that others can self-serve — Establish data quality, validation, observability, and monitoring frameworks (data auditing, alerting, anomaly detection, data lineage) — Investigate and resolve complex production issues: root cause analysis, performance bottlenecks, data integrity, fault tolerance — Document data flows, data dictionaries, architecture patterns, and operational runbooks Требования: — 5+ years of experience in data engineering (or similar) roles — Strong experience in ETL/ELT pipeline design, implementation, and optimization — Deep expertise in Python and SQL writing production-quality, maintainable, testable code — Experience with large-scale data warehouses (e.g. Databricks, BigQuery, Snowflake) — Solid grounding in software engineering fundamentals, data structures, and systems thinking — Hands-on experience in data modeling (dimensional modeling, normalization, schema design) — Experience building systems with real-time or streaming data (e.g. Kafka, Kinesis, Flink, Spark Streaming), and familiarity with CDC frameworks — Experience with orchestration / workflow frameworks (e.g. Airflow) — Familiarity with data governance, lineage, metadata, cataloging, and data quality practices

Location: Malmö About Neo4j: Neo4j is the graph intelligence platform that transforms data into knowledge to power the next generation of intelligent applications and AI systems. It includes enterprise-ready knowledge graphs for accurate, explainable, and governed AI; the most comprehensive, trusted, and easy-to-deploy graph capabilities across any environment and data source; and an unmatched ecosystem trusted by 84 of the Fortune 100 and supported by the world’s largest graph community. Intelligence that works. Results that matter. Built to work everywhere and integrate with everything across every cloud for dynamic, personalized, and autonomous AI systems. We deliver quicker results, contextual knowledge, and solutions that impact customers and employees across the business. Our Vision: At Neo4j, we have always strived to help the world make sense of data. As business, society and knowledge become increasingly connected, our technology promotes innovation by helping organizations to find and understand data relationships. We created, drive and lead the graph database category, and we’re disrupting how organizations leverage their data to innovate and stay competitive. The Team: Neo4j's managed cloud offering, Aura, brings the power of graph databases to the cloud. We're forming a new Product Analytics Platform team. As Neo4j Aura’s platform and product surface area have grown, so has the need for a consistent, purpose-built way to capture what's happening across them. Today, each product tracks its own data, but retrieving it can be difficult. This team will own the Analytics API and infrastructure that collects raw product analytics events from across Neo4j's products so every team integrates with one contract instead of inventing their own. The Role: In this role you’ll be one of the founding members of the Product Analytics Platform team. You’ll help define the technical foundations for how product analytics data is captured across Aura, building the Analytics API and underlying infrastructure that provides a consistent integration point for engineering teams across Neo4j. You’ll have significant ownership over the design of the API, balancing the needs of multiple product teams with the long-term maintainability of the platform. You’ll work closely with Product Management and other engineering teams to shape the solution, drive adoption, and evolve it as the platform grows. Primary responsibilities Design, build, and operate the Analytics API and the underlying infrastructure, defining the schema and integration contract used by teams across Aura. Make pragmatic architectural decisions around event ingestion, data contracts, reliability, and scalability as the platform grows. Build a simple, well-defined integration layer that makes it easy for product teams to send analytics events without coupling them to the underlying data infrastructure. Drive adoption across the Engineering organisation, working with teams to integrate with the API and providing the guidance and documentation they need. Keep product analytics and billing concerns cleanly separated, even where the underlying data flows or infrastructure may be similar. Evaluate build vs. buy across the existing analytics and data stack, including tools such as Segment and BigQuery, keeping the API focused on collection rather than reimplementing downstream capabilities. Partner closely with the team building the downstream data pipeline to ensure raw events are reliable, well-structured, and usable for analytics. Define and evolve the platform through RFCs, technical documentation, and close collaboration with Product Management and other engineering teams. What you'll need Strong backend software engineering experience, with Go as your primary language, building and operating APIs, services, or ingestion systems that other teams depend on. Experience designing well-defined APIs and integration contracts, including thinking about schemas, backwards compatibility, validation, and how other engineering teams will consume them. Experience building production services in Go, from smaller utilities and scripts through to larger, long-running services or microservices. Some experience with TypeScript or JavaScript and an ability to work comfortably in a codebase that spans backend and frontend technologies. This is primarily a backend role — you won't be responsible for UI development. Familiarity with event-driven systems, data ingestion, or data pipeline tooling such as Segment, BigQuery, or similar. Ability to work independently in a new area, make pragmatic technical decisions, and drive work forward with limited direction. Comfortable writing RFCs and technical documentation, and working across teams to establish and drive adoption of a new integration point. Clear, concise communication — a lot of this role involves defining contracts, documenting decisions, and helping other engineers integrate with what you build. What's nice to have Experience with observability, including tracing, logging, and metrics. Experience integrating with GCP, AWS, or Azure. Experience working with product analytics, event tracking, or telemetry systems. #LI-Hybrid Why Join Neo4j? Neo4j is, without question, the most popular graph intelligence platform in the world. We have customers in every industry globally, and our products are a proven product/market fit. Joining our team is an opportunity to shape the future of data and analytics. Below are just a few exciting facts about Neo4j. Neo4j is one of the fastest-scaling technology companies in this industry. It recently surpassed $200M in annual recurring revenue (ARR), doubling its ARR over the past three years. Raised the biggest funding round in database history ($325M Series F). Backed by world-class investors like Eurazeo, GV (formerly Google Ventures), and Inovia Capital, Neo4j has raised over $600M in funding and is currently valued at over $2Bn. This puts Neo4j among the most well-funded database companies in history. 84% of the Fortune 100 and 58% of the Fortune 500 use Neo4j. Examples include Boston Scientific , BT Group , Caterpillar , Cisco , Comcast , Department for Education UK , eBay , NBC News , Novo Nordisk , Worldline , and others . Co-founder and CEO Emil Eifrem has built an amazing culture that prides itself on relationships, inclusiveness, innovation, and customer success. Countless industry awards . Massive enterprises and individual developers/data scientists love Neo4j. A strong sense of community and ecosystem is built around the platform. A recent Forrester Total Economic Impact™ Study cited Neo4j as delivering 417% ROI to customers. Research shows that members of underrepresented communities are less likely to apply for jobs when they don’t meet all the qualifications. If this is part of the reason you hesitate to apply, we’d encourage you to reconsider and give us the opportunity to review your application. At Neo4j, we are committed to building awareness and helping to improve these issues. One of our central objectives is to provide an inclusive, diverse, and equitable workplace for everyone to develop their potential and have a positive, career-defining experience. We look forward to receiving your application. Neo4j Values: Neo4j is a Silicon Valley company with a Swedish soul. We foster collaboration and each of us is empowered to contribute and put our innovative stamp on projects. We hire candidates who reflect the following Neo4j core values: (we)-[:VALUE]->(relationships) (we)-[:FOCUS_ON]->(userSuccess) (we)-[:THRIVE_IN]->(:Culture {type: [‘Open’, ‘Inclusive’]}) (we)-[:ASSUME]->(:Intent {direction:’Positive’}) (we)-[:WELCOME]->(:Discussions {nature: ‘IntellectuallyHonest’}) (we)-[:DELIVER_ON]->(ourCommitments) Neo4j is committed to protecting and respecting your privacy. Please read the privacy notice regarding Neo4j's recruitment process to understand how we will handle the personal data that you provide. More information at www.neo4j.com . ©2026 Neo4j, Inc., Neo Technology®, Neo4j®, Cypher®, Neo4j Bloom™, Neo4j Graph Data Science Library™, Neo4j® Aura™, and Neo4j® AuraDB™ are registered trademarks or a trademark of Neo4j, Inc. All other marks are owned by their respective companies.

Location: London About Neo4j: Neo4j is the graph intelligence platform that transforms data into knowledge to power the next generation of intelligent applications and AI systems. It includes enterprise-ready knowledge graphs for accurate, explainable, and governed AI; the most comprehensive, trusted, and easy-to-deploy graph capabilities across any environment and data source; and an unmatched ecosystem trusted by 84 of the Fortune 100 and supported by the world’s largest graph community. Intelligence that works. Results that matter. Built to work everywhere and integrate with everything across every cloud for dynamic, personalized, and autonomous AI systems. We deliver quicker results, contextual knowledge, and solutions that impact customers and employees across the business. Our Vision: At Neo4j, we have always strived to help the world make sense of data. As business, society and knowledge become increasingly connected, our technology promotes innovation by helping organizations to find and understand data relationships. We created, drive and lead the graph database category, and we’re disrupting how organizations leverage their data to innovate and stay competitive. The Team: Neo4j's managed cloud offering, Aura, brings the power of graph databases to the cloud. We're forming a new Product Analytics Platform team. As Neo4j Aura’s platform and product surface area have grown, so has the need for a consistent, purpose-built way to capture what's happening across them. Today, each product tracks its own data, but retrieving it can be difficult. This team will own the Analytics API and infrastructure that collects raw product analytics events from across Neo4j's products so every team integrates with one contract instead of inventing their own. The Role: In this role you’ll be one of the founding members of the Product Analytics Platform team. You’ll help define the technical foundations for how product analytics data is captured across Aura, building the Analytics API and underlying infrastructure that provides a consistent integration point for engineering teams across Neo4j. You’ll have significant ownership over the design of the API, balancing the needs of multiple product teams with the long-term maintainability of the platform. You’ll work closely with Product Management and other engineering teams to shape the solution, drive adoption, and evolve it as the platform grows. Primary responsibilities Design, build, and operate the Analytics API and the underlying infrastructure, defining the schema and integration contract used by teams across Aura. Make pragmatic architectural decisions around event ingestion, data contracts, reliability, and scalability as the platform grows. Build a simple, well-defined integration layer that makes it easy for product teams to send analytics events without coupling them to the underlying data infrastructure. Drive adoption across the Engineering organisation, working with teams to integrate with the API and providing the guidance and documentation they need. Keep product analytics and billing concerns cleanly separated, even where the underlying data flows or infrastructure may be similar. Evaluate build vs. buy across the existing analytics and data stack, including tools such as Segment and BigQuery, keeping the API focused on collection rather than reimplementing downstream capabilities. Partner closely with the team building the downstream data pipeline to ensure raw events are reliable, well-structured, and usable for analytics. Define and evolve the platform through RFCs, technical documentation, and close collaboration with Product Management and other engineering teams. What you'll need Strong backend software engineering experience, with Go as your primary language, building and operating APIs, services, or ingestion systems that other teams depend on. Experience designing well-defined APIs and integration contracts, including thinking about schemas, backwards compatibility, validation, and how other engineering teams will consume them. Experience building production services in Go, from smaller utilities and scripts through to larger, long-running services or microservices. Some experience with TypeScript or JavaScript and an ability to work comfortably in a codebase that spans backend and frontend technologies. This is primarily a backend role — you won't be responsible for UI development. Familiarity with event-driven systems, data ingestion, or data pipeline tooling such as Segment, BigQuery, or similar. Ability to work independently in a new area, make pragmatic technical decisions, and drive work forward with limited direction. Comfortable writing RFCs and technical documentation, and working across teams to establish and drive adoption of a new integration point. Clear, concise communication — a lot of this role involves defining contracts, documenting decisions, and helping other engineers integrate with what you build. What's nice to have Experience with observability, including tracing, logging, and metrics. Experience integrating with GCP, AWS, or Azure. Experience working with product analytics, event tracking, or telemetry systems. #LI-Hybrid Why Join Neo4j? Neo4j is, without question, the most popular graph intelligence platform in the world. We have customers in every industry globally, and our products are a proven product/market fit. Joining our team is an opportunity to shape the future of data and analytics. Below are just a few exciting facts about Neo4j. Neo4j is one of the fastest-scaling technology companies in this industry. It recently surpassed $200M in annual recurring revenue (ARR), doubling its ARR over the past three years. Raised the biggest funding round in database history ($325M Series F). Backed by world-class investors like Eurazeo, GV (formerly Google Ventures), and Inovia Capital, Neo4j has raised over $600M in funding and is currently valued at over $2Bn. This puts Neo4j among the most well-funded database companies in history. 84% of the Fortune 100 and 58% of the Fortune 500 use Neo4j. Examples include Boston Scientific , BT Group , Caterpillar , Cisco , Comcast , Department for Education UK , eBay , NBC News , Novo Nordisk , Worldline , and others . Co-founder and CEO Emil Eifrem has built an amazing culture that prides itself on relationships, inclusiveness, innovation, and customer success. Countless industry awards . Massive enterprises and individual developers/data scientists love Neo4j. A strong sense of community and ecosystem is built around the platform. A recent Forrester Total Economic Impact™ Study cited Neo4j as delivering 417% ROI to customers. Research shows that members of underrepresented communities are less likely to apply for jobs when they don’t meet all the qualifications. If this is part of the reason you hesitate to apply, we’d encourage you to reconsider and give us the opportunity to review your application. At Neo4j, we are committed to building awareness and helping to improve these issues. One of our central objectives is to provide an inclusive, diverse, and equitable workplace for everyone to develop their potential and have a positive, career-defining experience. We look forward to receiving your application. Neo4j Values: Neo4j is a Silicon Valley company with a Swedish soul. We foster collaboration and each of us is empowered to contribute and put our innovative stamp on projects. We hire candidates who reflect the following Neo4j core values: (we)-[:VALUE]->(relationships) (we)-[:FOCUS_ON]->(userSuccess) (we)-[:THRIVE_IN]->(:Culture {type: [‘Open’, ‘Inclusive’]}) (we)-[:ASSUME]->(:Intent {direction:’Positive’}) (we)-[:WELCOME]->(:Discussions {nature: ‘IntellectuallyHonest’}) (we)-[:DELIVER_ON]->(ourCommitments) Neo4j is committed to protecting and respecting your privacy. Please read the privacy notice regarding Neo4j's recruitment process to understand how we will handle the personal data that you provide. More information at www.neo4j.com . ©2026 Neo4j, Inc., Neo Technology®, Neo4j®, Cypher®, Neo4j Bloom™, Neo4j Graph Data Science Library™, Neo4j® Aura™, and Neo4j® AuraDB™ are registered trademarks or a trademark of Neo4j, Inc. All other marks are owned by their respective companies.

Location: Warsaw, Poland WHAT IS BOX? Box (NYSE:BOX) is the leader in Intelligent Content Management. Our platform enables organizations to fuel collaboration, manage the entire content lifecycle, secure critical content, and transform business workflows with enterprise AI. We help companies thrive in the new AI-first era of business. Founded in 2005, Box simplifies work for leading global organizations, including JLL, Morgan Stanley, and Nationwide. Box is headquartered in Redwood City, CA, with offices across the United States, Europe, and Asia. By joining Box, you will have the unique opportunity to continue driving our platform forward. Content powers how we work. It’s the billions of files and information flowing across teams, departments, and key business processes every single day: contracts, invoices, employee records, financials, product specs, marketing assets, and more. Our mission is to bring intelligence to the world of content management and empower our customers to completely transform workflows across their organizations. With the combination of AI and enterprise content, the opportunity has never been greater to transform how the world works together and at Box you will be on the front lines of this massive shift. At Box, we believe unique and diverse experiences benefit our culture, our products, our customers, our company, and our world. We aim to recruit a passionate, high-performing workforce that reflects the world we live in. If you are head-over-heels about this role but unsure if you meet all the requirements, we encourage you to apply! Why Box needs you: You will join the newly established Data Foundations team within Core Data. We build, operate, and evolve the asynchronous data services that manage the scale and correctness of Box's growing data footprint — keeping it reliable, consistent, and synchronized across many systems and data stores. These services support a wide range of products and internal teams across Box. The team will take ownership of several existing services, improving their reliability, observability, scalability, and architecture. This is a great opportunity to work on distributed systems at scale and directly shape the team's technical direction. What you'll do: Own and evolve asynchronous data services that enable data synchronization across Box’s systems and data stores. Take ownership of existing services and improve their reliability, scalability, observability, and overall architecture. Design and implement new services and enhancements to accommodate growing data volumes and evolving platform requirements. Ensure data consistency and reliability across all data stores and downstream dependent systems. Collaborate with engineers and teams across Core Data and Box to understand data requirements, dependencies, and technical challenges. Help operate and improve production services by troubleshooting complex issues, triaging incidents, and addressing reliability and performance bottlenecks. Help drive the technical direction and engineering practices of the newly established Data Foundations team. Participate in the on-call rotation and remain available during assigned shifts to respond to, triage, and resolve production incidents. Who you are: We are an AI-first company. This means you approach your work with a growth mindset and find ways to leverage AI to help make faster, smarter decisions that will 10X your impact at Box. Bachelor's degree or equivalent in Computer Science, Mathematics or a related field. 5+ years of professional software development experience. Strong understanding of common algorithms, data structures and code design principles. Strong grasp of high-scale distributed systems. Strong programming background in strongly typed languages (e.g., Java, Scala). Hands-on experience with at least one relational database, such as MySQL or PostgreSQL, and familiarity with BigQuery. Strong sense of ownership, persistence and drive, strong communication skills. Actively use AI coding tools — such as Cursor, Claude Code, or Codex — as a core part of your development workflow, including requirements gathering, writing tests, and delivering code changes; Methodology Agile management - Scrum Issue tracking tool -Jira Knowledge repository - GitHubEnterprise Code reviews - GitHubEntreprise Version control system - GIT BENEFITS Check out the overview of Life at Box which include general perks and benefits. Box lives its values, with community and in-person collaboration being a core part of our culture. Boxers are expected to work from their assigned office a minimum of 3 days per week. Your Recruiter will share more about how we work and company culture during the hiring process. EQUAL OPPORTUNITY We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability, and any other protected ground of discrimination under applicable human rights legislation. Box strives to respect the dignity and ‎‎independence of people with disabilities and is committed to giving them the same ‎‎opportunity to succeed as all other employees. Inclusiveness is core to our culture at Box, and we strive to ensure you get the most from your interview experience. At Box, we believe unique and diverse experiences benefit our culture, our products, our customers, our company, and our world. We aim to recruit a passionate, high-performing workforce that reflects the world we live in. If you are head-over-heels about this role but unsure if you meet all the requirements, we encourage you to apply! For details on how we protect your information when you apply, please see our Personnel Privacy Notice . For more details on how Box Poland protects your information, please see our Supplemental Personnel and Candidate Privacy Notice . NOTICE OF USE OF AI At Box, we believe in the power of AI to improve the recruiting process while keeping people at the heart of every decision. For more information on the use of AI in our hiring process, please visit our webpage here . #LI-EMEA #LI -OB1 #LI-hybrid

Требования: — 2–3+ года опыта работы BI Analyst, Data Analyst или в аналогичной аналитической роли. — Практический опыт работы с SQL-ориентированными хранилищами данных, особенно Google BigQuery. — Продвинутые навыки SQL (BigQuery/PostgreSQL). — Сильные навыки Python, особенно Pandas и NumPy. — Свободное владение английским языком. Компания SEARADAR — европейская платформа для аренды яхт, которая помогает яхтсменам находить и бронировать парусные яхты и катамараны в более чем 20 направлениях.

Задачи: — Проектирование фич (вайрфреймы) и их тест на пользователях; — Работа с данными: SQL, ETL (1С, BigQuery), Python и BI-отчетность; — Анализ метрик (Retention и др.) и проверка гипотез; — Плотная работа с дизайном, разработкой и маркетингом. Требования: — Релевантный опыт или профильное обучение/курсы; — Аналитический склад ума + понимание основ дизайна; — Желание общаться с клиентами и быстро расти. Условия: — Аккредитованная ИТ-компания, офис в 2 мин. от метро; — Помощь наставника на старте; — График 5/2, скидки на SPA.

Location: CAN: Vancouver (333 Seymour St) Workplace: Hybrid The role and its impact As our Data Science Manager, you will look after a small team of 3 Data Scientists within Marketing Data Science, and you will stay close to the data yourself. You will be the people manager for data scientists based in Australia, the UK and South Africa, while personally owning marketing measurement for North America, including campaign and event measurement, churn analysis and funnel effectiveness. Your work shapes how quickly and how confidently marketing decisions get made at Xero. You will bring AI-driven insight generation and agentic analytics into everyday delivery, so the pod spends less time assembling reports and more time on the questions that move the business. You will also set the operating rhythm that helps a team spread across timezones prioritise well, share the load and keep delivery sustainable, and you will present complex analysis simply so that insight turns into decisions people act on. The team and how they connect Marketing Data Science helps Xero deliver digital, customer and marketing outcomes through insight that is increasingly AI-augmented. The wider team sits across Australia, New Zealand, the UK, South Africa and North America, so async ways of working, shared documentation and rotating meeting times are part of everyday life. You will work alongside other Data Science leads and partner closely with regional marketing teams, who rely on the pod as a thinking partner rather than a reporting service. Initially, you will focus on ➤ Working across SQL, Python, BigQuery, Google Analytics, Mixpanel and GTM, with dashboards and visualisation tools that people genuinely use. ➤ Campaign and event measurement for North America, so marketing teams can see what is working and where to invest next. ➤ Churn analysis and funnel effectiveness work that shapes acquisition, retention, engagement and personalisation. ➤ Embedding AI-driven insight generation and agentic analytics into the pod's day to day delivery, reducing time to insight across regions. Where and how you can work This role is based in Vancouver, where our office is set up for collaboration, focus work and connection, with meeting rooms, quiet spaces and desks ready whenever you drop in. We work flexibly and take a hybrid approach, and Boost Days are our weekly in-office days with catered meals and activities that support local small businesses. Here are some of the things we are looking for ➤ You will bring practical marketing measurement experience across campaigns, events, churn and funnel effectiveness, and you will Go Further by turning findings into recommendations people can act on. ➤ Working knowledge of SQL, solid Python, and a strong foundation in statistics, experimentation and causal thinking, with familiarity with Google Analytics, Mixpanel and BigQuery, form part of your everyday toolkit. ➤ You will Go Fast by using AI-driven and agentic analytics to cut the time it takes to get from question to insight. ➤ Coaching people you rarely share a timezone with comes naturally to you, and you will Go Together by creating rhythms that keep everyone connected and supported. ➤ Your communication flexes easily between technical and non-technical audiences, and between local and global ones. ➤ Comfort with ambiguity and loose direction will help you Go Bold, shaping what the team looks at next rather than waiting for the brief. Apply even if your experience isn't a perfect match! At Xero, we hire based on your skills, passion, and the unique perspective you can bring to enhance our culture and team. Compensation & Total Rewards At Xero, we value the impact and skills you bring to the team. We believe in a Total Package philosophy - ensuring your value is recognized through a holistic lens that includes base salary, performance incentives, and world-class benefits. The expected base salary range for this role is $140,000 - $190,000 CAD. Individual pay is determined by various factors, including geography, level of experience, and the specific skills you bring to the role. In addition to base pay, Xero’s roles are eligible for the following forms of variable compensation: - Variable Pay: Permanent employees are eligible to participate in our annual bonus and equity (RSU) programs. You may also be eligible for performance-based cash or equity (RSUs) incentives depending on your role level, and company performance. - Comprehensive Benefits: World-class health, wellness, and retirement programs. Xero Perks https://careers.xero.com/inside-xero/benefits-wellbeing/ including Wellbeing days, generous leave, and dedicated professional development budgets. This is a posting for an existing vacancy. Xero uses AI to help screen applications based on job-related qualifications. These tools assist our recruiters but do not make automated hiring decisions. A human recruiter reviews all candidates before any hiring action is taken.

Задачи: — Проектирование фич (вайрфреймы) и их тест на пользователях; — Работа с данными: SQL, ETL (1С, BigQuery), Python и BI-отчетность; — Анализ метрик (Retention и др.) и проверка гипотез; — Плотная работа с дизайном, разработкой и маркетингом. Требования: — Релевантный опыт или профильное обучение/курсы; — Аналитический склад ума + понимание основ дизайна; — Желание общаться с клиентами и быстро расти. Условия: — Аккредитованная ИТ-компания, офис в 2 мин. от метро; — Помощь наставника на старте; — График 5/2, скидки на SPA.