Аналитика данных вакансии

5+ years in Data Analytics / SaaS, Expert SQL, Python, dbt, Looker, Data Modelling, Analytics Engineering, ETL/ELT, A/B Testing, Experiment Design, Product Analytics, SaaS Metrics (MRR, NDR, Expansion, Contraction), Quasi-experimental Analysis, AI / MCP. ➕ Bonus: Data Science, ML, Staff/Principal level.

• Собирать и формализовывать требования: взаимодействовать с внутренними заказчиками, уточнять бизнес-задачи и переводить размытые запросы в понятную структуру отчёта; • Опыт работы в BI, аналитике данных, отчётности или смежной роли от 1,5–2 лет.

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 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: Europe (+/- 3 hours) Workplace: Remote 👋 A LITTLE ABOUT US We’re building the best platform for authoring, publishing, and maintaining world-class docs. Close to 40,000 sign-ups per month, over 100k Monthly Active Users, used by 2M+ people and thousands of teams like Zoom, FedEx, Nvidia, Snyk, and Google. We’re profitable, backed by P9 Capital, Notion Capital, and Fly VC, and our amazing team is spread across 15 countries in North America, Europe, and Asia. We’re looking for someone to make data a core lever for better product and business decisions.   This is a highly autonomous role. You’ll have the freedom to identify what we need, decide whether to build or buy, and turn data into measurable impact. 🙌 WHAT WILL YOU BE DOING? You’ll work at the intersection of data, product, and business, partnering closely with teams across GitBook. - Work with product and engineering to identify opportunities, define metrics, run experiments, and influence the roadmap - Turn messy questions into analysis, insights, and actions that improve product and company metrics - Collaborate with the Head of Data on our data architecture, foundations, and long-term vision - Evolve our data models, pipelines, instrumentation, and tooling as our needs change - Decide when to build, buy, or use lightweight solutions to solve data problems efficiently - Take our existing internal data agent to the next level, making it more useful and accessible across the company - Help teams across GitBook become more autonomous and effective in how they use data You’ll have the freedom to shape both what we build and how we work with data. 🧰 WHAT ENVIRONMENT WILL YOU BE WORKING IN? Our stack today includes: - GCP — BigQuery, Composer - dbt — Cloud / via Cursor - Replit — data apps & internal tooling - Tableau / Count — analysis & exploration - Amplitude / Segment — product & event data We don’t expect you to know everything. What matters is that you’re comfortable learning, evaluating tools, and evolving a modern data environment. 🧠 WHAT WE VALUE Product mindset: You’re curious about users and how the product works. You want to understand behavior, find opportunities, and see your work translate into better product decisions and measurable outcomes. Ownership & judgment: You’re comfortable taking an ambiguous problem and figuring out what to do next. You know when to go deep, move fast, build, buy, or keep things simple. Technical depth: You’re strong with SQL, data modeling, analytics, and modern data tooling. You can move comfortably between analysis, data foundations, and building practical solutions. Collaboration: You work well across product, engineering, growth, marketing, and leadership. You can challenge assumptions, communicate clearly, and bring people along. Bias for impact: You care about what changes because of your work — not just what gets shipped. 🎯 WHY THIS ROLE? You’ll have a broad scope and a lot of autonomy. Your work will directly influence: - Product and company metrics - Product priorities and roadmap decisions - Experiments and growth initiatives - Our data foundations and architecture - How people across GitBook use data and AI If you like owning problems, shaping the solution, and seeing your work directly impact the product, you’ll have a lot of room to make a difference here. 👥 Join GitBook at a pivotal time as we evolve our product and team. This is a chance to shape our product, craft, and culture https://www.gitbook.com/about#open-roles.

• Исследовать core banking систему, legacy- и внешние источники, восстанавливать их семантику; • Идти от целевой витрины к источнику: определять таблицы и логику сбора атрибутов;

Задачи: — Автоматизация финансовых расчетов и онлайн балансов — Автоматизация финансовых и операционных процессов, сверок и отчетности — Работа с базами данных PostgreSQL, структурами данных, запросами и финансовыми дата-мартами — Разработка скриптов, сервисов, ботов и внутренних инструментов автоматизации — Работа с ценообразованием, спредами, комиссиями, маржами и юнит-экономикой — Мониторинг в реальном времени и оповещения о отклонениях маржи — Использование ChatGPT / LLM инструментов и API для кодирования, анализа, автоматизации и документации — Взаимодействие с финансовым отделом для преобразования бизнес-требований в надежные технические решения Требования: — Сильные навыки SQL и профессиональные навыки Python — Хорошие знания PostgreSQL / баз данных — Практический опыт работы с автоматизацией, скриптами, пайплайнами или сервисами — Понимание архитектуры данных и логики расчетов — Опыт использования ChatGPT / LLM как рабочего инструмента — Способность работать самостоятельно с комплексными или неоднозначными требованиями — Системное мышление и ответственность за результат, а не только за код — Свободное владение русским и английским языками Условия: — Работа в быстрорастущей финтех-среде — Высокая степень ответственности и реальное влияние — Работа на стыке финансов, технологий и искусственного интеллекта — Возможность создавать и улучшать системы с нуля — Международная команда и современный офис

О компании OperAI — интерактивная система на базе ИИ для управления операционной деятельностью компании. Цель — создать «второй мозг для CEO», сделать процессы прозрачными и управляемыми, помочь руководителям видеть риски, договорённости и реальное состояние компании. Сейчас запускают пилоты с первыми командами и проверяют продуктовые гипотезы. Требования: — Опыт в продуктовой или data-аналитике от 3 лет — Опыт проектирования архитектуры данных и построения аналитической инфраструктуры с нуля — Умение создавать модели, схемы и пайплайны для сбора, обработки и хранения структурированных и неструктурированных данных, в том числе с AI — Практический опыт работы с неструктурированными данными: переписками, расшифровками встреч, документами и корпоративными коммуникациями — Опыт построения процессов разметки данных: разработка схем разметки, критериев качества и инструкций для разметчиков — Уверенное владение SQL и Python для анализа и обработки данных — Понимание ETL/ELT-процессов, требований к качеству данных и принципов Data Governance — Глубокое понимание принципов работы LLM, embeddings, RAG и AI-агентов — Умение проектировать метрики и системы оценки AI-функций: точность, полнота, галлюцинации и реальная польза для пользователя — Готовность брать на себя ответственность и строить аналитическую функцию продукта с нуля Задачи: — Проектировать систему внутренней аналитики — Проектировать структуру хранения данных: события, встречи, задачи, решения, коммуникации и связи между ними — Помогать формировать единую модель данных для AI-функций продукта — Оценивать качество работы LLM: точность извлечения фактов, полноту, галлюцинации и полезность результатов — Разрабатывать подходы к разметке данных и созданию датасетов для тестирования AI-системы — Участвовать в проектировании AI-пайплайнов, RAG, embeddings и систем поиска по корпоративным данным — Находить закономерности в операционных процессах клиентов и превращать их в продуктовые решения — Определять ключевые продуктовые метрики и строить дашборды для команды и фаундеров — Анализировать поведение пользователей, пилотные запуски и продуктовые гипотезы — Вместе с разработчиками улучшать качество, наблюдаемость и надёжность продукта Будет большим плюсом: — Опыт проектирования архитектуры AI- или ML-систем — Работа с LLM API, векторными базами, LangChain/LangGraph или аналогичными инструментами — Опыт построения систем мониторинга и оценки качества LLM — Понимание операционной деятельности компаний: задач, встреч, решений, ответственности, сроков и отчётности — Опыт работы с BI-инструментами и продуктовыми системами аналитики — Опыт в B2B SaaS, стартапах или продуктах для руководителей и команд — Опыт взаимодействия с data engineering, ML и backend-командами Условия: — Equity-модель компенсации на начальном этапе — опцион — Возможность с нуля построить аналитику AI-продукта — Высокое влияние на архитектуру, продуктовые решения и развитие компании — Прямая работа с фаундерами — Пилотные клиенты и реальные данные для проверки гипотез — Гибкий формат работы и быстрый цикл принятия решений — Возможность вырасти в Head of Data / AI Analytics по мере развития проекта

Компания edna — один из ведущих поставщиков решений в области цифровых коммуникаций, а также продуктов для обеспечения безопасного доступа и аутентификации пользователей.

Компания Symfa ищет Middle Data Quality Engineer / BA для удалённой работы из стран ЕС, Грузии или Сербии. В обязанности входит обеспечение качества данных, анализ бизнес-требований и взаимодействие с командами разработки. Кандидат должен обладать опытом работы с данными, навыками анализа и пониманием процессов контроля качества. Требуется умение работать с инструментами для проверки и валидации данных, а также способность выявлять и документировать проблемы с данными. Важны коммуникационные навыки для взаимодействия с различными отделами и понимание бизнес-процессов в сферах финансов, страхования, здравоохранения, недвижимости, туризма и HoReCa.

Задачи: — Аналитика данных в компании Rambler Требования: — Опыт от 1 года — Работа с Python и SQL — Использование визуализаторов данных Условия: — Работа в Москве — Гибридный формат работы — Зарплата обсуждается индивидуально

Location: United States Workplace: Remote BUILDING THE FUTURE OF OPEN FINANCE Payward - the parent company behind Kraken, NinjaTrader, Breakout, xStocks, Payward Services and CF Benchmarks - has spent the last 15 years building one of the most modern and globally accessible financial infrastructure platforms in the industry, built to advance an open, global financial system. THE TEAM Founded in 2011, Kraken is one of the world's longest-standing crypto platforms, trusted by over 10 million individuals and institutions across the globe. It offers spot trading, margin, futures, staking, and OTC services, with products built for both individual investors and institutional clients. We're looking for a Data Analyst, Regulatory Analytics and Reporting to join our team remotely! The #1 goal of this role is to turn complex compliance and regulatory data into clear, actionable insights that protect the business, satisfy regulators, and support senior leadership in making high-stakes decisions. You'll work at the intersection of data engineering, regulatory reporting, and compliance operations in a fast-paced, high-scrutiny environment where accuracy and audit-readiness are non-negotiable. Beyond technical expertise, success in this role depends on being a true business partner who combines data-driven insights with deep knowledge of compliance regulations and frameworks. You'll own end-to-end data management from building and maintaining datasets and pipelines to designing dashboards and insights for compliance and leadership teams. You will also automate compliance processes and workflows to boost efficiency and minimize human error. If you combine exceptional data analysis expertise with solid knowledge of compliance regulations, a keen eye for regulatory risk, and the drive to shape the future of crypto with a top-tier brand - this is your next mission!   THE OPPORTUNITY - Own the design of dashboards, compliance metrics, and regulatory reports, turning data into actionable insights for stakeholders, cross-functional partners, and regional compliance officers - Automate end to end compliance related data solutions, governance controls, and automations to boost efficiency and minimize manual intervention - Dive into large, complex datasets to turn findings into scalable, audit-ready data models that facilitate regulatory reporting and analytics - Translate regulatory and business requests into accurate reports and automated data pipelines using Python, SQL, DBT and Airflow - Lead cross-functional projects with analysts, engineers, and compliance teams to build and maintain high-quality data pipelines and sources - Coordinate client data quality monitoring by identifying issues, flagging remediations, and partnering with responsible teams to ensure timely resolution - Support ad-hoc data requests for audits, exams, risk assessments, and day-to-day business needs - Deliver insights that shape compliance direction through clear, data-driven storytelling   WHAT YOU BRING - 5+ years of experience in data analysis and data management within financial services or a regulated industry, with hands-on experience in compliance and regulatory reporting across frameworks such as AML, KYC, Sanctions, MiCA, or MiFID - Solid understanding of regulatory risk and the ability to operate effectively in high-scrutiny, audit-ready environments - Expert SQL skills including complex joins, CTEs, and analytical functions, paired with proficiency in Python libraries such as pandas, matplotlib, and plotly - Hands-on experience using dbt or similar tooling to model and organize high fidelity data for reporting and decision-making - Experience setting up and managing data pipelines with a strong understanding of data workflow orchestration - Proficiency with BI and data visualization tools such as QuickSight, Power BI, or Superset to build executive-facing dashboards - Strong communicator who can simplify complex data ideas for both technical and non-technical audiences - Alignment with the values outlined in Kraken's Tentaclements document   NICE TO HAVES - Degree in Mathematics, Statistics, Finance, Information Systems, or a related technical field - Passion for continuous learning and approaching problems with a beginner's mindset - Solid understanding of the cryptocurrency space and how financial markets function Unless a specific application deadline is stated in the job posting, applications are accepted on an ongoing basis. Please note, applicants are permitted to redact or remove information on their resume that identifies age, date of birth, or dates of attendance at or graduation from an educational institution. We consider qualified applicants with criminal histories for employment on our team, assessing candidates in a manner consistent with the requirements of the San Francisco Fair Chance Ordinance. OUR COMMITMENT Payward is powered by people from around the world and we celebrate the diverse talents, backgrounds, contributions, and unique perspectives that everyone brings to the table. We hire based on merit, seeking out people with the right abilities, knowledge, and skills for the job. We encourage you to apply for roles where you don't fully meet the listed requirements, especially if you're passionate or knowledgeable about crypto. We may ask candidates to complete job-related skills or work-style assessments as part of our hiring process. These assessments evaluate competencies relevant to the role and are applied consistently across candidates for similar positions. Results are considered alongside experience and interviews, and are not the sole basis for any employment decision. As an equal opportunity employer, we don't tolerate discrimination or harassment of any kind, whether based on race, ethnicity, age, gender identity, citizenship, religion, sexual orientation, disability, pregnancy, veteran status, or any other protected characteristic as outlined by federal, state, or local laws. Stay connected Follow us on Twitter https://twitter.com/krakenfx Learn on the Kraken Blog https://blog.kraken.com/#:~:text=Enter%20your%20email%20address Connect on LinkedIn https://www.linkedin.com/company/kraken-exchange/ Candidate Privacy Notice https://www.kraken.com/legal/candidate-privacy-notice

Location: United Kingdom; Brazil; Ireland; Cyprus; Portugal; Lithuania; Czech Republic; Canada; Poland Workplace: Remote BUILDING THE FUTURE OF OPEN FINANCE Payward - the parent company behind Kraken, NinjaTrader, Breakout, xStocks, Payward Services and CF Benchmarks - has spent the last 15 years building one of the most modern and globally accessible financial infrastructure platforms in the industry, built to advance an open, global financial system. THE TEAM Founded in 2011, Kraken is one of the world's longest-standing crypto platforms, trusted by over 10 million individuals and institutions across the globe. It offers spot trading, margin, futures, staking, and OTC services, with products built for both individual investors and institutional clients.   We're looking for a Data Analyst, Regulatory Analytics and Reporting to join our team remotely! The #1 goal of this role is to turn complex compliance and regulatory data into clear, actionable insights that protect the business, satisfy regulators, and support senior leadership in making high-stakes decisions. You'll work at the intersection of data engineering, regulatory reporting, and compliance operations in a fast-paced, high-scrutiny environment where accuracy and audit-readiness are non-negotiable. Beyond technical expertise, success in this role depends on being a true business partner who combines data-driven insights with deep knowledge of compliance regulations and frameworks. You'll own end-to-end data management from building and maintaining datasets and pipelines to designing dashboards and insights for compliance and leadership teams. You will also automate compliance processes and workflows to boost efficiency and minimize human error. If you combine exceptional data analysis expertise with solid knowledge of compliance regulations, a keen eye for regulatory risk, and the drive to shape the future of crypto with a top-tier brand - this is your next mission!   THE OPPORTUNITY - Own the design of dashboards, compliance metrics, and regulatory reports, turning data into actionable insights for stakeholders, cross-functional partners, and regional compliance officers - Automate end to end compliance related data solutions, governance controls, and automations to boost efficiency and minimize manual intervention - Dive into large, complex datasets to turn findings into scalable, audit-ready data models that facilitate regulatory reporting and analytics - Translate regulatory and business requests into accurate reports and automated data pipelines using Python, SQL, DBT and Airflow - Lead cross-functional projects with analysts, engineers, and compliance teams to build and maintain high-quality data pipelines and sources - Coordinate client data quality monitoring by identifying issues, flagging remediations, and partnering with responsible teams to ensure timely resolution - Support ad-hoc data requests for audits, exams, risk assessments, and day-to-day business needs - Deliver insights that shape compliance direction through clear, data-driven storytelling   WHAT YOU BRING - 5+ years of experience in data analysis and data management within financial services or a regulated industry, with hands-on experience in compliance and regulatory reporting across frameworks such as AML, KYC, Sanctions, MiCA, or MiFID - Solid understanding of regulatory risk and the ability to operate effectively in high-scrutiny, audit-ready environments - Expert SQL skills including complex joins, CTEs, and analytical functions, paired with proficiency in Python libraries such as pandas, matplotlib, and plotly - Hands-on experience using dbt or similar tooling to model and organize high fidelity data for reporting and decision-making - Experience setting up and managing data pipelines with a strong understanding of data workflow orchestration - Proficiency with BI and data visualization tools such as QuickSight, Power BI, or Superset to build executive-facing dashboards - Strong communicator who can simplify complex data ideas for both technical and non-technical audiences - Alignment with the values outlined in Kraken's Tentaclements document   NICE TO HAVES - Degree in Mathematics, Statistics, Finance, Information Systems, or a related technical field - Passion for continuous learning and approaching problems with a beginner's mindset - Solid understanding of the cryptocurrency space and how financial markets function Unless a specific application deadline is stated in the job posting, applications are accepted on an ongoing basis. Please note, applicants are permitted to redact or remove information on their resume that identifies age, date of birth, or dates of attendance at or graduation from an educational institution. We consider qualified applicants with criminal histories for employment on our team, assessing candidates in a manner consistent with the requirements of the San Francisco Fair Chance Ordinance. OUR COMMITMENT Payward is powered by people from around the world and we celebrate the diverse talents, backgrounds, contributions, and unique perspectives that everyone brings to the table. We hire based on merit, seeking out people with the right abilities, knowledge, and skills for the job. We encourage you to apply for roles where you don't fully meet the listed requirements, especially if you're passionate or knowledgeable about crypto. We may ask candidates to complete job-related skills or work-style assessments as part of our hiring process. These assessments evaluate competencies relevant to the role and are applied consistently across candidates for similar positions. Results are considered alongside experience and interviews, and are not the sole basis for any employment decision. As an equal opportunity employer, we don't tolerate discrimination or harassment of any kind, whether based on race, ethnicity, age, gender identity, citizenship, religion, sexual orientation, disability, pregnancy, veteran status, or any other protected characteristic as outlined by federal, state, or local laws. Stay connected Follow us on Twitter https://twitter.com/krakenfx Learn on the Kraken Blog https://blog.kraken.com/#:~:text=Enter%20your%20email%20address Connect on LinkedIn https://www.linkedin.com/company/kraken-exchange/ Candidate Privacy Notice https://www.kraken.com/legal/candidate-privacy-notice

Задачи: — Исследовать данные источников, искать зависимости и находить инсайты; — Проводить самые разнообразные исследования, основанные на данных; — Создавать и регулярно обновлять аналитическую отчётность.

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: — Глубокая работа с данными в финтех области. — Проверка полноты, корректности и актуальности данных для расчёта банковских показателей риска PD, LGD, EAD. Требования: — Опыт работы аналитиком данных не менее 3 лет (middle). — Опыт работы в финтехе или банковской сфере. — Владение SQL на уровне сложных запросов. — Коммерческий опыт работы с Oracle. — Опыт применения Airflow (основы). — Опыт работы с Service Desk или аналогами. — Знание FineBI (основы). — Коммерческий опыт с Informatica ETL и Python. — Опыт работы с банковскими показателями риска PD, LGD, EAD. — В резюме должны быть подробно описаны обязанности и проекты, раскрыт опыт применения технологий. — Указание компаний, образования и возраста. — Отсутствие нецензурных выражений и неподобающих контактов в резюме. — Минимум два года работы на последних трёх местах. Условия: — Жёсткий отбор, важна правильная подача резюме и раскрытие опыта.

SQL Developer: — разработка, оптимизация и сопровождение SQL-кода систем: написание сложных SQL-запросов, разработка процедур, функций, триггеров и views, работа с индексами, создание миграций — анализ execution plans, проведение code review и рефакторинг — участие в разборе production-инцидентов Требования: — коммерческий опыт SQL-разработки — уверенное понимание реляционных БД, транзакций и блокировок — опыт работы с миграциями и Git Database Engineer / DBA: — оптимизация запросов и структуры БД — анализ execution plans, поиск блокировок и deadlock — контроль изменений в Test и Production — backup/restore, репликация и обеспечение отказоустойчивости Требования: — опыт работы DBA или Database Engineer — уверенный SQL — понимание индексов, locking, High Availability Product Analyst: — анализ продуктовых метрик B2C-направления: воронки, retention, когорты — расчет unit-экономики и финтех-специфичных метрик (GMV, payment success rate) — настройка трекинга в Amplitude — построение дашбордов и написание SQL-запросов к PostgreSQL — автоматизация анализа на Python Требования: — опыт с SQL и продуктовой аналитикой — Python (pandas/numpy) — понимание unit-экономики и финтех-метрик Общее: — будет плюсом опыт работы в FinTech, банках или платёжных системах, а также с высоконагруженными БД Условия: — формат работы: офис в Караганде или удалённо по всему Казахстану — официальное трудоустройство по ТК РК — понятный онбординг — обучение и развитие за счёт компании — частичная компенсация занятий спортом — корпоративные активности

Задачи: — Сбор и подготовка данных: извлечение информации из различных источников и приведение данных к единому формату; — Глубокий анализ данных и поиск зависимостей: исследование массивов данных, выявление скрытых корреляций, причинно-следственных связей и критических паттернов, необходимых для решения бизнес-задач.

Задачи: — обновление информации в базе; — проверка записей; — поиск дублирующихся данных; — подготовка простых отчётов; — поддержание порядка в системе. Условия: — Новым сотрудникам дают инструкции и вводное обучение; — Работа проходит полностью онлайн, без необходимости ездить в офис.