Вакансии

Location: United States About Us Fanatics is a leading global sports platform redefining how fans connect with the teams, players and moments they love. Our businesses — Fanatics Commerce, Fanatics Collectibles, Fanatics Betting & Gaming, Fanatics Events, Fanatics Advertising, Fanatics Markets and Fanatics Studios — maximize the presence and reach for hundreds of partners globally and create a one-stop experience that allows fans to buy, bet, collect, and earn, while engaging with sports in deeper, more immersive ways. This includes licensed fan gear and lifestyle products, sportsbook and iGaming, physical and digital trading cards, live events, premium sports entertainment content, credit card offerings and more. Together, they form a powerful, integrated ecosystem — connected by the Fanatics ONE loyalty program and FanCash, the currency of sport — delivering personalized experiences to over 100 million fans worldwide. Fanatics holds partnerships with over 900 sports properties, from professional leagues and players’ associations to teams, colleges and retail partners; over 7,000 athletes and celebrities; and over 2,000 retail locations, including all Lids stores. Backed by a team of more than 22,000 employees, Fanatics is committed to relentlessly enhancing the fan experience. Overview You lead the team that owns the analytical backbone of how the Operations Department understands its own performance. Sitting within the Strategic Operations & Analytics team, you manage a small team of analysts whose work is used every day by leaders across CX, Fraud, Payments, WFM, and VIP. You also remain directly involved in the work. This is a hands-on manager role, roughly an even split between leading the team and doing the work. You will write and optimize SQL, review your team's models and analyses, and own complex cross-functional initiatives end-to-end. You will set the bar for that work, develop the people doing it, and be accountable for what the team ships. You’ll work closely with the Sr. Manager of Strategic Operations, who provides strategic direction, executive alignment, and support navigating priorities and cross-functional dependencies. We are actively building toward an AI-first way of operating, and this team is central to it. The right person is technically sharp, relentlessly detail-oriented, and genuinely curious about both what the data is saying and how AI changes the way we find and act on answers. You move fast, own ambiguous problems, and hold yourself and your team to a high bar. What You'll Do Leading the team Manage, coach, and develop a team of 2–3 analysts, including goal setting, regular feedback, performance management, and career development. Set and hold the technical and analytical bar for the team's output, through direct review of SQL, dbt models, dashboards, and analyses. Manage day-to-day prioritization across the team’s queue of Ops stakeholder requests, escalating broader tradeoffs and resourcing decisions to the Sr. Manager when needed. Analytical work and insights Personally lead complex, cross-functional analytical initiatives end-to-end from an ambiguous business problem through scoping, analysis, and a decision-ready recommendation. Diagnose root causes in the data rather than symptoms, and translate the diagnosis into a concrete recommendation. Lead scenario analysis and impact sizing for product initiatives, operational changes, and staffing decisions - shaping how the question itself gets framed. Set standards and guide the team’s approach to Ops dashboards and reporting, ensuring leaders have clear visibility into performance through well-defined KPIs and documented assumptions. Present findings to operational stakeholders and senior leadership, and defend your methodology under scrutiny. Analytics foundations and automation Own the quality and reliability of the dbt models and analytical datasets maintained by the Ops Analytics team. Partner with central data teams as needed on upstream pipelines, data-platform needs, etc., representing Ops requirements and escalating reliability issues when needed. Help the team replace recurring manual data pulls with maintainable reporting, automation, and alerting. AI enablement Partner with the Sr. Manager and Strategic Operations leaders on the Ops analytics AI roadmap, bringing an analytics perspective to use-case prioritization, data readiness, and validation. Ensure the team provides the trusted data, metrics, and evaluation frameworks needed to support agent development. Track and report on the performance of deployed agents against agreed-upon Ops goals. Help establish where AI can be trusted in analytical workflows and where human review and judgment should remain in the loop. Required Qualifications 6+ years in analytics, business intelligence, data analytics, or strategic operations. 2+ years directly managing analysts as direct reports , including performance management, hiring, and career development. This is not a first-time manager role. Advanced, hands-on SQL. You write, QA, and optimize complex queries yourself today, and you review others' work at that level. This role includes a live, on-screen technical exercise. Working fluency in dbt: You can build and review models, make sound design decisions, and identify issues in your team’s work. Demonstrated experience owning complex, cross-functional projects end-to-end, from ambiguous ask to stakeholder-ready recommendation. Experience building and maintaining dashboards in Sigma, Tableau, or a comparable tool. Strong stakeholder management: you communicate complex problems clearly to senior leaders and propose alternative approaches when the data warrants it. High attention to detail - you and your team catch data quality issues before they surface in leadership reporting. Preferred Qualifications Experience supporting AI/ML workflows, agent builds, or automation initiatives. Python or a similar scripting language for data manipulation and automation. Familiarity with operational KPIs across customer support, fraud, payments, or workforce management. Experience in gaming, fintech, or sports. Bachelor's degree in Analytics, Computer Science, Statistics, Economics, or a related field. Depending on the role, your interview and onboarding experience may include in-person components, such as onsite interviews or Launching into Better: LIVE—a multi-day cultural immersion in New York City for full-time, non-seasonal hires. These sessions are designed to build connection and bring our culture to life, though specific travel and participation requirements will be confirmed based on your role and location. Your recruiter will provide clear guidance at each stage of the process. For information about our benefits, please visit https://benefitsatfanatics.com/ #LI-JM3 Ranges will change based on country and state of residence, which are reflected in Geographical Zones defined by Fanatics Betting and Gaming. The range incorporates all of our Geographical Compensation Zones and is subject to change as the Zone associated with the actual offer is confirmed. In addition to the base and bonus, full-time employment, and more. For information about our benefits, please visit https://benefitsatfanatics.com/ Salary Range $138,000 — $170,000 USD By submitting your application, you agree to our terms of service and acknowledge you have read our Candidate Privacy Policy. Fanatics will never ask candidates for money, banking information, or other sensitive personal information as part of the interview process. If you receive a suspicious message claiming to be from Fanatics recruiting, please do not respond and report it here .

Задачи: — Глубокая аналитика продаж - работа с большими массивами данных. — Выгрузка, обработка и анализ информации о продажах. — Ведение текущей отчетности. Требования: — Опыт работы аналитиком. — Высшее образование. — Готовность работать с большим объемом информации (от 500 тыс. строк). — Уверенный пользователь MS Excel.

Senior Marketing Analytics Consultant в Improvado. Требования: владение SQL, Looker, Power BI или Tableau, уровень английского C1. Компания Improvado — сервис для агрегации маркетинговых данных, среди клиентов ASUS, illy, Wrike и другие. Размер команды — 150 человек, инвестиции — $34M.

Задачи: — Описание текущих процессов AS-IS и целевых процессов TO-BE — Анализ существующих модулей кредитного процесса: взаимодействия, участников, частоты и форматов обмена данными — Проработка целевых сценариев: маршрутизация, маппинг и обработка данных, обработка ошибок, унификация модулей и процессов — Формирование road map изменений и задач по процессам — Совместная с ИТ оценка задач и согласование изменений со всеми заинтересованными участниками — Участие в архитектурной миграции на Единый мидл — Анализ интеграций и взаимодействий между системами, в том числе API — Разработка и согласование нормативной и регламентной документации — Подготовка пользовательских инструкций и материалов по изменениям — Проведение тестирования нового функционала совместно с пользователями и клиентами — Анализ внешних источников, конкурентов и технологий Требования: — Опыт в сфере кредитования или создания кредитных продуктов и процессов — Практический опыт описания и оптимизации бизнес-процессов — Опыт внедрения изменений процессов в банках — Опыт работы с архитектурными решениями, миграциями систем/данных и интеграциями по API — Опыт работы в Agile-командах — Навыки моделирования процессов с использованием BPMN, SIPOC, IDEF0 — Опыт анализа рынка, конкурентов и технологий, подготовки аналитических материалов — Опыт разработки и согласования внутренних регламентов и нормативной документации — Умение готовить презентации, схемы, аналитические и сводные таблицы — Уверенное владение Word, Excel, PowerPoint, Visio Условия: — Гибридный формат работы: 2–3 дня в офисе — Оформление по ТК РФ или ИП — Техника предоставляется — ДМС со стоматологией — Компенсация фитнеса — Скидки на обучение, кино, Skyeng — Индексация зарплаты

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 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

Обязательные требования: — Локация/Гражданство: только РФ — Опыт системного анализа от 5 лет, из них 2-3 года на лидирующих позициях — Опыт менторства Junior и Middle аналитиков — Экспертное знание BPMN/UML/ER — Продвинутый уровень SQL — Опыт проектирования интеграционных решений (REST, RabbitMQ) — Понимание микросервисной архитектуры, опыт участия в разработке и валидации архитектурных решений — Умение писать техническую документацию и ставить задачи разработчикам/QA — Навыки презентации решений — Подтвержденный банковский опыт работы с клиентскими данными в KYC процессе — Знание профильного законодательства, в частности, 115 ФЗ и требований по защите персональных данных - 152-ФЗ — Опыт использования LLM для написания сложных SQL-запросов и валидации данных — Умение составлять эффективные промпты — Критическая оценка результатов ИИ, понимание ограничений нейросетей Задачи: — Анализ требований от заказчиков и согласование ФЗ — Разработка технических заданий и спецификаций — Проектирование интеграций и API — Описание структуры данных и моделей — Взаимодействие с разработчиками, тестировщиками и архитекторами — Участие в приёмке готовых решений — Ведение проектной документации Условия: — Формат работы: удалённо — Вилка до 190к на руки

Задачи: — Полное ведение приёмки новых версий моделей: определение необходимых замеров, запуск оценок, объединение результатов и подготовка заключения о качестве модели. — Развитие системы метрик для оценки реального качества моделей: определение обязательных сигналов, объединение разных оценок, расчет статистической значимости, отличение настоящих изменений от случайного шума. — Работа с данными и инструментами приёмки: определение состава оценочных наборов, контроль покрытия важных сценариев, управление версиями, обеспечение воспроизводимости замеров. — Развитие инструментов: создание автоматических оценщиков на базе LLM (LLM-as-a-judge), применение гибридных методов. — Управление командой и процессами: формирование команды аналитиков приёмки, распределение направлений работы, наставничество, планирование ресурсов. Требования: — Опыт управления командой аналитиков не менее двух лет. — Умение работать с данными и извлекать из них практический результат. — Знание математической статистики и теории вероятностей. — Навыки программирования на Python и знание алгоритмов. — Умение формулировать обоснованные заключения и защищать их перед заказчиками в условиях неполных данных. Условия: — Ежегодные медицинские чекапы, йога и психотерапия. — Другие бонусы, полный список которых доступен по ссылке https://yandex.ru/jobs/pages/benefits.

Задачи: — Описание текущих процессов AS-IS и целевых процессов TO-BE — Анализ существующих модулей кредитного процесса: взаимодействия, участников, частоты и форматов обмена данными — Проработка целевых сценариев: маршрутизация, маппинг и обработка данных, обработка ошибок, унификация модулей и процессов — Формирование road map изменений и задач по процессам — Совместная с ИТ оценка задач и согласование изменений со всеми заинтересованными участниками — Участие в архитектурной миграции на Единый мидл — Анализ интеграций и взаимодействий между системами, в том числе API — Разработка и согласование нормативной и регламентной документации — Подготовка пользовательских инструкций и материалов по изменениям — Проведение тестирования нового функционала совместно с пользователями и клиентами — Анализ внешних источников, конкурентов и технологий Требования: — Опыт в сфере кредитования или создания кредитных продуктов и процессов — Практический опыт описания и оптимизации бизнес-процессов — Опыт внедрения изменений процессов в банках — Опыт работы с архитектурными решениями, миграциями систем/данных и интеграциями по API — Опыт работы в Agile-командах — Навыки моделирования процессов с использованием BPMN, SIPOC, IDEF0 — Опыт анализа рынка, конкурентов и технологий, подготовки аналитических материалов — Опыт разработки и согласования внутренних регламентов и нормативной документации — Умение готовить презентации, схемы, аналитические и сводные таблицы — Уверенное владение Word, Excel, PowerPoint, Visio Условия: — Гибридный формат работы: 2–3 дня в офисе — Оформление по ТК РФ или ИП — Предоставляется техника — ДМС со стоматологией — Компенсация фитнеса — Скидки на обучение, кино, Skyeng — Индексация заработной платы

Задачи: — стажировка по профессии системный аналитик с последующим трудоустройством Требования: — техническое образование или опыт в IT/смежной сфере (тестирование, разработка и т.д.) — базовое понимание SQL и BPMN или желание разобраться — Москва или готовность переехать при получении релевантного оффера — готовность работать в офисе после стажировки (как минимум, в период испытательного срока) — у мужчин — приписное/военный билет — коммуникабельность, логика, здравый интерес к продуктам и бизнесу Условия: — удаленно на время стажировки (срок — 2-3 месяца), далее гибрид/офис в Москве — вилка после стажировки 170 000 ₽ – 230 000 ₽

Требования: — Гражданство и локация РФ. — Опыт системного анализа от 5 лет, из них 2-3 года на лидирующих позициях. — Экспертное знание BPMN, UML, ER. — Продвинутый уровень SQL. — Опыт проектирования интеграционных решений (REST, RabbitMQ). — Понимание микросервисной архитектуры, опыт участия в разработке и валидации архитектурных решений. — Умение писать техническую документацию и ставить задачи разработчикам/QA. — Навыки презентации решений. — Опыт менторства Junior и Middle аналитиков. — Подтвержденный банковский опыт работы с клиентскими данными в KYC процессе. — Знание профильного законодательства, в частности, 115 ФЗ и требований по защите персональных данных - 152-ФЗ. — Опыт использования LLM для написания сложных SQL-запросов и валидации данных. — Умение составлять эффективные промпты. — Критическая оценка результатов ИИ, понимание ограничений нейросетей. Обязанности: — Анализ требований от заказчиков и согласование ФЗ. — Разработка технических заданий и спецификаций. — Проектирование интеграций и API. — Описание структуры данных и моделей. — Взаимодействие с разработчиками, тестировщиками и архитекторами. — Участие в приёмке готовых решений. — Ведение проектной документации. Условия: — Удалённая работа. — Вилка до 200к рублей на руки.

Задачи: — Готовить и поддерживать ежедневную, еженедельную и ежемесячную операционную отчётность для руководителей и функциональных команд. — Разрабатывать и автоматизировать отчёты в Excel с использованием Power Query, сложных формул и макросов. — Писать SQL-запросы для выборки, агрегации и трансформации данных; поддерживать и развивать витрины данных в PostgreSQL и ClickHouse. — Анализировать отклонения ключевых метрик, выявлять причины изменений и готовить рекомендации для бизнеса. — Разрабатывать и поддерживать дашборды в Grafana, настраивать алерты по критическим отклонениям показателей. — Собирать данные из нескольких источников, обеспечивать их стыковку, проводить валидацию и сверку метрик между системами. — Участвовать в настройке и поддержке ETL/ELT-процессов при необходимости. — Собирать и уточнять требования к отчётам и метрикам у бизнес-заказчиков. — Документировать логику расчётов, определения метрик, бизнес-правила и инструкции для пользователей. — Консультировать коллег по работе с отчётами и дашбордами. Требования: — Опыт работы аналитиком данных, бизнес-аналитиком или аналитиком операционной отчётности от 1–2 лет. — Законченное высшее образование. — Продвинутый SQL и уверенное владение Excel. — Опыт работы с реляционными базами данных и BI-инструментами. — Понимание принципов контроля качества данных, валидации показателей и воспроизводимой отчётности. — Умение работать с несколькими источниками данных и понимание различий OLTP- и OLAP-нагрузок. — Внимательность к деталям, системное мышление, ответственность за сроки и качество результата. — Умение объяснять аналитические выводы понятным для бизнеса языком. Будет преимуществом: — Опыт работы с ETL/ELT-инструментами: Apache NiFi, Airflow или аналогами. — Базовые навыки Python и pandas для анализа данных и автоматизации задач. — Опыт работы с Git, Jira и Confluence. — Опыт в сферах закупок, банковских гарантий, финансовых операций, документооборота или операционной отчётности.

В крупном коммерческом банке требуется системный аналитик уровня Senior для участия в разработке интеграционных решений. Кандидат должен иметь опыт работы в сфере FinTech и владеть навыками системного анализа. Работа предполагает удалённый формат с возможностью сотрудничества на международном уровне.

Компания HelioCore, работающая в сфере HRTech, расширяет команду и приглашает на позиции Business Analyst и HR Analyst уровня Middle и Senior. Вакансии предполагают работу в офисе в Варшаве, Польша. Основные задачи включают анализ данных и процессов в области HR, выявление возможностей для оптимизации и внедрение решений с использованием искусственного интеллекта и автоматизации.

GB2B empowers iGaming companies to deliver seamless player experiences and operate with absolute confidence. As we continue to scale and build the next generation of high-performance platform technologies, we’re looking for a Data Analyst to join our mission and turn complex player, operational, and financial data into actionable strategic insights. Задачи: — Гипотезы и рекомендации для продуктовых и бизнес-команд

Обязанности: — Анализ данных по транзакциям, платежам, клиентам и продуктам (карты, переводы, кредиты, цифровые каналы) из АБС и DWH — BI-дашборды и регулярная отчётность: воронки, retention/churn, доходность — Ad-hoc исследования, A/B-тесты, выявление аномалий и фрода — SQL-запросы, витрины данных, ETL, данные для отчётности ЦБ РУз — Гипотезы и рекомендации для продуктовых и бизнес-команд

В роли продуктового аналитика предстоит работать с приложением для сотрудников, используя данные и аналитику пользовательского поведения для его развития. Основная задача — анализировать информацию, чтобы улучшать продукт и принимать обоснованные решения на основе полученных данных.

Требуется системный аналитик с навыками работы с Microsoft Dynamics NAV, SOAP, REST, API-интерфейсами, Apache Kafka, Confluence и Jira. Условия: — Полный рабочий день — Возможна удалённая работа Зарплата до 140 000 ₽.