Дата-инженер вакансии - удалённо

Location: European Union; Ukraine Workplace: Remote ABOUT US Ruby Labs is a leading tech company that creates and operates innovative consumer products. We offer a diverse range of opportunities across the health, education, and entertainment industries. Our innovative teams are driving the future of consumer-led products, and we're always looking for passionate individuals to join us. Learn more about our story at: https://rubylabs.com/about-us/ ABOUT THE ROLE We are looking for a Data Analytics Engineer to build, optimize, and maintain the analytical data layer that powers decision-making across the organization. You will transform business requirements into reliable, well-modeled datasets using dbt, SQL, and cloud data warehouses (BigQuery, ClickHouse). This role combines data modeling, documentation, data quality design, and close collaboration with analysts, engineers, and product teams to ensure consistent, trustworthy reporting. You will serve as the bridge between raw data ingestion and analytics — shaping how data is structured, validated, and delivered. Responsibilities - Design, build, and maintain dbt models (staging, core, marts) based on business needs - Develop scalable SQL transformations and ensure efficient, cost-effective query performance - Implement data quality tests (dbt tests, custom checks, anomaly detection) and maintain high data reliability. - Create and maintain documentation for datasets, data lineage, business logic, and transformation rules - Collaborate with Data Engineers on improving raw data structures, ingestion patterns, and source readiness - Partner with Data Analysts, Product Managers, and Marketing teams to translate business requirements into clean, reusable data models - Monitor data pipelines and marts, resolve issues, and prevent inconsistencies in reporting - Support governance initiatives: naming conventions, taxonomy, schemas, metric definitions, and standardization - Contribute to continuous improvement of our data modeling frameworks, performance tuning, and technical decision-making Requirements - Strong SQL experience with a deep understanding of analytical modeling concepts - Hands-on experience with dbt (models, tests, docs, macros) - Experience working with cloud data warehouses such as BigQuery or ClickHouse - Understanding of data modeling patterns - Familiarity with data engineering concepts (ELT/ETL, orchestration, version control, CI/CD) - Experience with data quality frameworks or implementing validation logic - Ability to translate complex business processes into clean analytical datasets. - Strong documentation skills and attention to detail - Experience collaborating with cross-functional teams and supporting analysts with reliable data - Knowledge of Python is a plus (but not required) Nice To Haves - Experience with orchestration tools (Airflow, Dagster) - Experience working with marketing, product, or billing datasets - Exposure to metric layers, semantic layer design, or BI governance - Basic understanding of observability tooling (Grafana, Prometheus, ... ) - Experience optimizing BigQuery costs or ClickHouse performance LOCATION Ruby Labs operates within the CET (Central European Time) zone. Applicants from any country are welcome to apply for the position as long as they are located within approximately ± 4 hours of CET. This ensures optimal collaboration and communication during working hours. BENEFITS Discover the perks of being part of our vibrant team! We offer: - Remote Work Environment: Embrace the freedom to work from anywhere, anytime, promoting a healthy work-life balance. - Unlimited PTO: Enjoy unlimited paid time off to recharge and prioritize your well-being, without counting days. - Paid National Holidays: Celebrate and relax on national holidays with paid time off to unwind and recharge. - Company-provided MacBook: Experience seamless productivity with top-notch Apple MacBooks provided to all employees who need them. - Flexible Independent Contractor Agreement: Unlock the benefits of flexibility, autonomy, and entrepreneurial opportunities. Benefit from tax advantages, networking opportunities, reduced employment obligations, and the freedom to work from anywhere. Read more about it here: https://docs.google.com/document/d/1nkrN76JlZkbKj9WSOhlT1_mni_CZeDkHdwfIjPXVwvk/preview?tab=t.0#heading=h.ndsdl4wapxtt Be part of our fast-growing team and seize this excellent opportunity for personal and professional growth! INTERVIEW PROCESS After submitting your application, we conduct a thorough review which typically takes 3 to 5 days, but may occasionally take longer due to the volume of applications received. If we see a potential fit, we proceed with the following steps: - Recruiter Screening (40 minutes) - Technical Interview (90 minutes) - Final Interview (60 minutes) LIFE AT RUBY LABS At Ruby Labs, we move fast, aim high, and expect the same from our team. We’re not here to play small—we’re here to build, grow, and win. That means we look for people who are ambitious, driven, and ready to give their best every single day. This is a place for individuals who thrive under pressure, embrace challenges, and see opportunity in every obstacle. If you’re hungry to achieve, motivated by impact, and want to grow at the speed of your own ambition, Ruby Labs offers the platform to make it happen. Here, effort is matched with reward. We recognize those who go all in and deliver results, and we create space for people who want more—more responsibility, more growth, and more success. #LI-Remote

Задачи: — Участвовать в формировании модели данных — Проектировать и разрабатывать витрины и потоки данных на основе потребностей бизнес-пользователей — Участвовать в проектах по развитию Data Platform — Писать скрипты SQL и Python (опционально) — Прорабатывать интеграции с внешними системами для Data Platform Требования: — Свободно владеть SQL — Опыт работы с DWH в качестве аналитика или разработчика минимум 5 лет — Понимать теорию СУБД и ETL-процессов — Знание Python или другого языка программирования — Опыт проектирования объектов БД на основании бизнес требований Условия: — Работа в офисе или удаленно по договоренности — Платформа обучения и развития «Т‑Банк Апгрейд» (курсы, тренинги, вебинары, базы знаний) — Поддержка менторов и наставников, помощь в карьерном развитии — Оформление полиса ДМС со стоматологией и страховки от несчастных случаев — Льготное страхование близких — Возможность работы в аккредитованной ИТ-компании — Льготные тарифы на продукты Т‑Банка — Частичная компенсация затрат на спорт — Well-being-программа для улучшения психологического и физического здоровья, а также юридической и финансовой поддержки — Три дополнительных дня отпуска в год — Достойная зарплата, обсуждаемая на собеседовании

Location: Remote USA Workplace: remote ABOUT YOU We are looking for a Head of Fund Analytics & Automation who is a hands-on builder, rigorous with data, and fluent in finance conversations to join our Credit Fund team in the office of the Chief Credit Officer. The best candidate will be someone who thrives in a fast-paced, highly collaborative, and exceptionally dynamic setting and is excited to design, build, and operate the fund's entire data and automation stack end to end — and then own it as the fund goes live. The credit fund is being built with a deliberately small team. Instead of hiring several analysts and operations staff, we want one senior technical hire who can do both: engineer the platform and sit across the table from investors and borrowers. Underwriting here is built on real payment telemetry — including Xsolla transaction data — and that data edge is the foundation of this role. Strong SQL, Python, and production automation skills are essential, along with real experience in portfolio analytics, market risk, or credit. The ability to ship audit-grade systems and then defend the numbers in front of LPs and borrowers will be key to your success in this role. The role starts as a builder and becomes the owner-operator of the platform, with a growing seat in underwriting decisions and, as the platform and deal volume grow, scope to hire and lead a small team. If you're passionate about applying AI-governed automation to private credit and love building the systems that let a lean team punch far above its weight, we would love to hear from you! ABOUT US Xsolla is a global commerce company with robust tools and services to help developers solve the inherent challenges of the video game industry. From indie to AAA, companies partner with Xsolla to help them fund, distribute, market, and monetize their games. Grounded in the belief in the future of video games, Xsolla is resolute in the mission to bring opportunities together, and continually make new resources available to creators. Headquartered and incorporated in Los Angeles, California, Xsolla operates as the merchant of record and has helped over 1,500+ game developers to reach more players and grow their businesses around the world. With more paths to profits and ways to win, developers have all the things needed to enjoy the game. For more information, visit xsolla.com. Responsibilities First 6 months — priorities in order - Deal pipeline live: origination intake through credit committee, with the full audit trail. - Scoring data layer: borrower financials and payment telemetry ingested, stored, and under documented data contracts. - LP / fundraising pipeline instrumented, and first investor reporting shipped. Explicitly out of scope: legal documentation, fund administration, and accounting — these sit with external providers. Phase 1: Build (through first close) Deal pipeline management - Design and run the full deal workflow: origination intake, screening, scoring, credit committee, closing. - Implement it as governed automation (workflow orchestration such as n8n or similar, with LLM-assisted steps where they add value): every automated output validated against a defined schema, low-confidence results routed to human review, and a complete audit trail suitable for LP due diligence. Scoring and underwriting data layer - Build the ingestion and storage layer for borrower financials and payments telemetry (including Xsolla transaction data) — PostgreSQL or equivalent, ETL pipelines, materialized views, documented data contracts. - Develop credit scoring and forecasting models in Python, with proper train/test discipline, leakage and PII exclusion, and ongoing monitoring for drift and degradation. - Maintain evaluation and regression checks so model and automation quality is measured continuously, not assumed. Fundraising / LP pipeline - Build and operate the investor pipeline: CRM automation, conference and referral pipeline tracking, follow-up orchestration, and data room preparation and upkeep. - Instrument the pipeline so we always know conversion rates, stage aging, and next actions per LP. Investor and borrower dashboards - Ship reporting for both audiences: fund-level metrics for investors, facility-level metrics for borrowers. - Numbers must reconcile to source systems and be reproducible outside the BI tool — audit-grade, not demo-grade. Tableau / Power BI or a lightweight web dashboard, whichever fits. Reliability, cost, and security of the stack - Budget caps and cost monitoring on all AI-assisted automation; regression canaries before changes ship. - Access control and data protection appropriate for fund data: deny-by-default permissions, audit logs, strict handling of LP identities, borrower financials, and deal terms. - Documented runbooks and handover-ready documentation as part of "done" — the stack must be operable by someone other than its author. Phase 2: Operate (post-close) - Move into an operating role on the underwriting side: portfolio monitoring, covenant and collateral tracking, scenario and stress analysis. - Extend the platform to other investment types and support the capital formation team with the same pipeline and reporting infrastructure. Client and investor facing This is not a back-office role. The person will join investor and borrower meetings, present the dashboards and the numbers behind them, and field diligence questions directly. Fluency in finance conversations is as important as engineering. The person will also respond to LP operational due diligence questionnaires on the data and automation stack. Qualifications and Skills Required - 7+ years across data analytics / data engineering, including recent hands-on experience building and running production automation (not prototypes or notebooks). - Proven production experience with LLM-based automation: schema-validated outputs, human review gates, evaluation and regression testing, cost-tiered model routing. - Strong SQL and Python; ownership of a PostgreSQL (or similar) data platform end to end — ETL, materialized views, performance tuning, data contracts. - Workflow orchestration experience (n8n, Airflow, or comparable). - BI and dashboarding: Tableau, Power BI, Qlik, or equivalent web dashboards; a track record of reporting that executives actually used for decisions. - Security discipline for sensitive data: role-based access, deny-by-default policies, audit trails, PII handling. - Finance background: degree in finance or quantitative field plus real experience in portfolio analytics, market risk, or credit — able to hold their own in an underwriting or investor conversation. - Strong written and spoken English; comfortable presenting to senior external audiences. Preferred - Direct exposure to private credit, lending, or fund operations. - Forecasting and statistical modeling track record (capacity planning, SLA/risk forecasting, or similar). - Experience with embeddings / semantic search and multi-model AI setups. - Web development ability (React / TypeScript or similar) for internal tools and dashboards. - Experience in audited or regulated environments (SOC 2, fund audits, or equivalent). Benefits We are passionate about fostering a supportive environment for our team, so we prioritize the physical, mental, and emotional well-being of our employees and their families through a comprehensive Benefits Program. This includes 100% company-paid medical, dental, and vision plans, unlimited Flexible Time Off, and a personalized career roadmap for each employee. By investing in professional development through training and educational opportunities, we ensure that our team thrives both personally and professionally. Together, we’re not just building a business; we’re cultivating a community that values creativity, collaboration, and the transformative power of play. Equal Employment Opportunity Statement Xsolla is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate based on race, color, religion, sex, national origin, age, disability, sexual orientation, gender identity, or any other characteristic protected by law. We consider qualified applicants with criminal histories in accordance with the Fair Chance Act. Criminal History Consideration For the Head of Fund Analytics & Automation — Credit Fund position, we will conduct a background check that may include the following: Criminal history check Employment verification Education verification Credit history check Relevance to Job Responsibilities The background check is relevant to this position because of the following role responsibilities: Handling sensitive financial information and supporting fund analytics, financial models, and deal data Accessing confidential company data Ensuring compliance with regulatory requirements Rights Under the Fair Chance Act Applicants are encouraged to inquire about their rights under the Fair Chance Act. If you have questions regarding our hiring practices, please contact careers@xsolla.com. By submitting the following job application form, you consent to Xsolla processing your data for career-related inquiries and potential employment opportunities. We process your data in accordance with this Xsolla Privacy Notice for Job Applicants. Please direct any inquiries regarding your data privacy to careers@xsolla.com.

Location: New York; Canada; United States Workplace: Remote WHO ARE WE? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Why this role? We're building the foundational infrastructure that will define how the world thinks about and deploys AI, and we want the sharpest, most curious people to help us do it. As a member of our Analytics & Data Insights team, you'll tackle the kind of problems that don't have textbook answers yet, launch products that didn't exist a year ago, and help enterprises understand what foundational AI actually means for their bottom line. As a Data Engineer, you will: - Work directly on new customer experiences built on one of the most advanced AI systems in the world - Collaborate daily with researchers and engineers who are some of the best in the world at what they do - Run implementations end-to-end and see initiatives through to real outcomes - Partner across research, marketing, sales, and finance to help define how Cohere grows, with your recommendations feeding directly into products and strategy You may be a good fit if you have: - 5+ years of experience working on production-grade data processing systems - Strong command of Python and SQL - Experience with distributed data processing frameworks such as Apache Beam, Spark, or Flink, alongside large-scale data system design - The ability to transform unstructured data into performant datasets in relational databases and blob storage - Familiarity with modern analytics stack tooling such as BigQuery, Airflow, or dbt - Knowledge of Java or Golang (nice to have) - Experience with Kubernetes (nice to have) - Genuine excitement about AI - you follow the research, have opinions, and enjoy being in the weeds - Comfort operating at the edge of what's known, with a desire to build something genuinely new rather than optimise what already exists FULL-TIME EMPLOYEES AT COHERE ENJOY THESE PERKS: - A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch. - Full health and dental benefits, including a separate budget for mental health. - RRSP matching, 401K, Pension Scheme. - 100% Parental Leave top-up for up to 6 months, for either parent. - Annual enrichment benefits: Arts & culture, fitness/wellness, quality time, and a workspace improvement credit. Education & learning stipend for conferences, courses, and coaching. - 6 weeks of paid vacation (30 working days!) - Budget for traveling to other offices if you are remote, plus an annual company offsite. HOW AND WHERE WE WORK: - Cohere is remote-friendly, but we also have offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul with more opening soon. - For those in the office: a daily lunch program, plenty of snacks, and regular community and social events. - For those not near an office: a co-working benefit so you can work alongside others in your city. - Everyone receives a $500 home office stipend to set up your workspace properly. If any of the above doesn’t line up exactly with your experience, we still encourage you to apply. We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form https://docs.google.com/forms/d/12a6IrLdF3kI2nonKSr4tiFuz18rLQbaeYV-JM9L4o9Q/edit, and we will work together to meet your needs. We may use AI-enabled tools to screen and assess applicants against the criteria for this position. This helps our recruiters identify potentially qualified candidates, but it doesn't limit the applications our recruiters may review or consider. Beware of Scams: Cohere will never ask for payment or third-party services (e.g., CV writing) as part of our hiring process. All legitimate roles are listed on the Cohere careers page and LinkedIn only, with all communications from Cohere employees coming from an @cohere.com or @cw.cohere email alias. If jobs are viewed on other sites then please verify these through our official careers https://cohere.com/careers page.

Simple Life is the #1 AI-powered health coaching app for adults who want to lose weight and enjoy a healthier lifestyle—without the stress or extremes. Our mission is to empower people to feel their best every day. By challenging traditional, restrictive approaches, Simple offers a more sustainable method grounded in ease, personalization, and real-life support. Simple has had over 17 million downloads and more than 300,000 5-star reviews, having helped millions lose weight successfully and sustainably. Simple has earned recognition as Best Virtual Coach and one of the Top 100 AI companies — all thanks to a dedicated global team driving real impact. With SIMPLE as a partner in their pocket, users feel cared for and empowered to embrace — and stick to — new healthy habits. Simple is looking for a Senior Data Engineer to join our Tech team. Push the pace of innovation and build a future of a healthier world with us! Задачи: — Design, build, and maintain streaming pipelines. — Develop custom ingestion pipelines from third-party APIs, webhooks, and SDKs. — Migrate existing data ingestion pipelines from SaaS tools to our own infrastructure. — Deliver data to the raw layer of our Snowflake warehouse with schema consistency, idempotency, and resilience to malformed or late data. — Evolve the existing AWS streaming stack (Kinesis, SQS, Lambda, API Gateway), improving cost efficiency, observability, and resilience. — Maintain infrastructure as code (Terraform) for ingestion and processing resources. — Monitor pipelines, troubleshoot, and resolve incidents. — Contribute to our ongoing work on safe data access for AI agents and tooling. — Continuously improve ingestion architecture, tooling, and engineering practices. Требования: — 5+ years of commercial Python experience in a backend or data engineering role. — Solid understanding of distributed systems architecture, including load balancing, sharding, and trade-offs between synchronous and asynchronous processing. — Hands-on experience with cloud services, preferably AWS (Lambda, API Gateway, containers in ECS/EKS). — Experience building streaming pipelines (Kinesis, SQS, or equivalents). — Experience with infrastructure as code (Terraform or equivalents). — Working knowledge of SQL: ability to read and write queries, including joins and window functions. — Commercial experience with real-world production data: ability to design systems that gracefully handle messy, duplicated, delayed, or malformed input. — Solid understanding of how data flows through production systems, including the ability to anticipate and prevent downstream issues. — Operational mindset: comfortable owning production systems and monitoring them. — Ability to make architectural decisions independently and review the work of teammates. Желательно: — Strong SQL and data modeling skills. — Experience with Snowflake and dbt. — Experience with workflow orchestrators (Airflow, Dagster, Prefect, or equivalents). — Experience working with analytics and product teams as a data provider. — Experience working with sensitive or regulated data. Условия: — Open-minded teams, a welcoming and inclusive company culture. — Competitive salary package based on expertise, skillset, and impact plus stock options. — In-office, remote and hybrid work opportunities. — Equipment needed for productivity. — Premium SIMPLE subscription. — 21 days annual leave plus bank holidays. — Flexible hours focusing on results, not desk time. О компании и ценностях: — Data-driven decision making. — Focus on impact and speed. — Ownership and responsibility. — Encouragement to push limits and innovate. — Team accountability and collaboration.

Задачи: — Проектирование, разработка и сопровождение ETL/ELT-пайплайнов с использованием Snowflake и SQL — Управление облачной инфраструктурой на AWS (S3, Glue, Lambda, CloudWatch, IAM) — Написание чистого и поддерживаемого кода на Python — Автоматизация развертывания и настройки инфраструктуры через Terraform — Оркестрация рабочих процессов с помощью Apache Airflow — Мониторинг и устранение неполадок в пайплайнах, контроль качества данных и производительности систем — Совместная работа с дата-сайентистами, аналитиками и разработчиками для создания масштабируемых решений Требования: — 5+ лет коммерческого опыта в Data Engineering или смежных областях — Глубокие знания Snowflake и продвинутый уровень владения SQL — Реальный опыт работы с AWS (S3, Glue, Lambda, CloudWatch, IAM) — Уверенное владение Python для обработки данных и автоматизации — Практический опыт использования Terraform (инфраструктура как код) — Опыт работы с Apache Airflow для оркестрации пайплайнов — Отличные коммуникативные навыки и английский язык уровня C1 — Способность работать самостоятельно в распределённой команде Будет плюсом: — Опыт интеграции и разработки REST API — Знание CircleCI или других CI/CD-инструментов — Опыт работы в полностью удалённых международных командах — Понимание принципов управления данными и безопасности Локация: Удалённо (предпочтительно за пределами РФ и РБ, желательно страны Латинской Америки или Канада) График работы: по восточному времени США (EST) до 17:00, с привязкой к американскому рабочему календарю. Уровень английского языка: C1 (продвинутый профессиональный)

Location: North America Workplace: Remote At Linear, we're building the product development system for teams and agents. AI is fundamentally changing how software gets built, and we’re shaping the tools this new era requires. Founded in 2019, Linear has become the platform of choice for more than 40,000 companies (including OpenAI, Coinbase, and Ramp) to plan, build, and ship their products. Today, our team is distributed across North America, Europe, and Australia, and we’re continuing to grow internationally. What unites us is relentless focus, fast execution, and a deep care for software craftsmanship. The Data team works across Linear, supporting Product, Engineering, and GTM. We own our data pipelines, warehouse, dashboards, analysis, and integrations with third-party tools. As a small team, we focus on building systems that make data accessible and useful across Linear. We’re looking for someone who wants to help shape how we architect, build, and use data as we grow. Location & work mode Linear is a remote-first company, with optional co-working offices in San Francisco, New York, and London. This role is open to candidates based in North America. You can work from anywhere within this region. We value deep focus and async collaboration, with intentional moments to connect in person through team off-sites, optional co-working, and occasional travel. What you’ll do - Work across Product and GTM (Marketing, Sales, Customer Success, and Finance) to turn ambiguous questions and operational needs into useful metrics, models, analyses, and workflows - Build and maintain dbt models and pipelines that create trusted views of our product, customers, and business - Design clear, maintainable data models and improve the testing, documentation, performance, and reliability of our data stack - Build dashboards and self-service reporting in Metabase and Hex, and dig deeper when the answer requires more than a chart - Operationalize data through reverse ETL and partner with GTM Engineering on the scoring, segmentation, automations, and internal tools that help our teams scale - Balance fast, pragmatic answers with durable solutions, recognizing when a one-off request should become a reusable model or workflow - Find ways to leverage emerging tools, LLMs, and coding agents to accelerate development, analysis, testing, and documentation while maintaining a high bar for correctness What we're looking for - 8+ years of experience in analytics engineering, data analytics, or data engineering, ideally at a fast-moving software company - Exceptional SQL and strong hands-on experience with dbt and a modern cloud data warehouse - Track record of owning data projects end-to-end, from shaping an ambiguous problem to shipping something people rely on - Strong data modeling judgment: you think clearly about grain, reusable components, interfaces, dependencies, and maintainability without relying on a single prescribed methodology - Analytical judgment: you know when to answer quickly, when to investigate deeply, and how to turn complex findings into a clear recommendation - Comfortable moving between technical implementation and business context, from debugging a data model to understanding product adoption or sales efficiency - High ownership mentality: self-directed, pragmatic, and willing to challenge a request or approach when something does not make sense - Strong communication skills and experience partnering directly with both technical and non-technical teams - Comfortable using LLMs and coding agents as part of your day-to-day development workflow Our tech This stack reflects the systems you’ll work in. You’re not expected to have experience with everything listed, but you should be comfortable learning quickly and working across the full lifecycle of data. - Data Warehouse: Snowflake, dbt Cloud - Dashboards / Analysis: Metabase, Hex - ETL / rETL: Hevo, Fivetran - GTM tools: Hubspot, Pocus, Clay What we offer We're a small, focused team that cares deeply about the quality of our work and the people we do it with. Here's what you can expect: - Competitive salary and equity - Employee-friendly equity terms including early exercise in the US and extended exercise windows - Daily meal and coffee stipend on every workday - Paid co-working space or desk - Health coverage (based on country requirements) - 5 weeks paid vacation, plus local statutory holidays - 4 months paid parental leave - Paid month off after 4 years & every 2 years thereafter - Regular team events and off-sites - Remote-first with no required commute Learn how we think and work - A story about our mission: Read Me https://linear.app/readme - Our hiring process: How we hire at Linear https://linear.app/now/how-we-hire-at-linear - How we work: Designing remote work at Linear https://linear.app/now/designing-remote-work-at-linear - How Linear uses Linear: How our Customer Experience team works in Linear https://linear.app/now/cx-in-linear - A video series: Conversations on Quality https://linear.app/quality - Building our teams: Why and how we do work trials at Linear https://linear.app/blog/why-and-how-we-do-work-trials-at-linear - Our recent tender offer: Sharing Linear’s growth with the people building it https://linear.app/now/sharing-growth-with-the-people-building-linear Linear is an equal opportunity employer. We do not discriminate based on race, color, religion, gender identity or expression, sexual orientation, national origin, age, disability, veteran status, or any other protected characteristic under applicable law.

RELOCATION TO GERMANY adjoe builds the technologies behind mobile apps growth and monetization. With our core product Playtime Arcade, we’ve become the global leader in rewarded advertising, an ad unit built on a simple premise: users earn real in-app rewards for engaging with new apps. The result is one of the most effective value exchanges in adtech, connecting advertisers and publishers with over 770 million users annually. Architecting Intelligence to Optimize 200M+ Daily Decisions As the intelligence core of our engineering organization, our Data Science team doesn’t just deploy models, we engineer the fundamental decision engine that powers our platform. At a scale of 770 million users and 100,000+ predictions per second, we are solving a multi-objective optimization problem that balances user incentives, advertiser ROI, and long-term platform health in real time. Our architecture is built on a 1PB+ behavioral data lake, providing the high-fidelity input necessary to train deep learning models that predict individual user engagement with precision. We aren’t just optimizing clicks, we are dynamically calculating optimal reward structures to sustain a global value exchange. Engineered for performance, our stack leverages Tensorflow and PyTorch for model training, NVIDIA Triton to achieve sub-100ms inference. We own the full ML lifecycle from high-level research and feature engineering to deployment and A/B experimentation. Here, you will find the autonomy, the data depth, and the massive scale required to solve the most complex optimization challenges in the adtech ecosystem. The data infrastructure behind this runs at real scale: 2TB ingested in real time every day, 100+ Airflow jobs and data pipelines, and ML models handling p99 latency of 100ms across 100,000+ predictions per second. As a Data Engineer on this team – you’ll be building and maintaining the infrastructure that makes them possible: the Feature Store, the data pipelines, the data quality systems, and the data architecture that data scientists depend on to move fast. Your Mission & Who We Are Looking For: Data engineering at ML scale. You have 5+ years in data engineering on a modern stack, with proven experience handling systems that process several TB of data per day and thousands of events per second. Flink is your default for real-time. You have hands-on experience with Apache Flink for stateful stream processing and real-time feature computation. You’ll translate Data Science requirements into high-performance streams, enable Data Scientists to contribute new features and implement features yourself when needed. Pipeline bottlenecks don’t survive long around you. You know how to identify and eliminate inefficiencies in complex ETL jobs, whether handling high-volume batch processing or real-time streaming systems like Kafka. At adjoe, data velocity is critical: slow pipelines directly impact model freshness and prediction quality at sub-100ms inference. You think in layers, not tables. You move beyond raw data to design robust, multi-layered data architectures using dbt, and can reason confidently about storage formats, Data Lakehouse models, and what belongs where in a 1PB+ data lake. Familiarity with Medallion Architecture is a strong plus. The bridge between Data Science and Backend. You’ve worked closely with Data Scientists on online ML systems with low latency requirements, and understand what it takes to make experimental research production-ready. Strong Java required; Go or Python a plus. Owns quality, not just delivery. You implement data validation, monitoring, and governance processes in production, because in a system where model inputs directly drive advertiser ROI, data integrity isn’t optional. Experience with Airflow, Kubeflow, AWS, Terraform, and Kubernetes is a strong plus. 🌎 We welcome applications from talent worldwide and provide relocation support to Hamburg, Germany for those ready to join our team. What’s in It for You? At adjoe, you’re not here to just close JIRA tickets, you’re helping build the infrastructure behind one of the most impactful platforms in adtech. The systems you work on will reach hundreds of millions of users and power billions of decisions every day. Go Big. Own projects with impact on 770M users and push adtech boundaries. Move Fast. Ship solutions multiple times a day, learn from results, and keep momentum. Be Direct. Solve problems openly and collaborate across teams. Thrive Together. Grow with a diverse, global team of people from over 40 different countries that learn from each other. Have Fun. Celebrate wins, enjoy daily victories, and bring your energy.

adjoe builds the technologies behind mobile apps growth and monetization. adjoe Ads is a programmatic in-app advertising platform connecting advertisers and publishers through Android and iOS SDKs. The User Experience team owns native Kotlin and Swift code running inside publisher apps, participating in 120 million daily auctions and capturing behavioral signals powering 50 billion predictions per day. The team also manages analytics dashboards, investigation tools, and data frameworks for real-time revenue performance, infrastructure cost visibility, and data quality assessments. Your Mission & Who We Are Looking For: — 5+ years of data analysis experience with senior leadership, mentoring junior/mid-level analysts, and bridging business ideas to technical solutions. — Design and automate reliable, scalable data pipelines and workflows using dbt and Airflow for real-time performance reporting, data quality control, and regulatory compliance. — Mastery of SQL (Window Functions, CTEs, deeply nested JSON/Map types) to optimize Trino/Athena queries over 5TB+ daily data volumes for speed and cost-efficiency. — Deep Python expertise for complex, memory-efficient data manipulation and statistical modeling to handle massive daily data volumes. — Apply Python-driven statistical methods (Power Analysis, P-values, Bayesian frameworks) for rigorous evaluation of SDK-side experiments and feature rollouts. — Combine data visualization and technical storytelling to translate complex analyses and operational findings into actionable recommendations for stakeholders. Relocation support to Hamburg, Germany is provided for candidates worldwide ready to join the team.

Задачи: — Управлять полным циклом извлечения данных с комплексных веб-сайтов, обеспечивая полное покрытие, точность и надежную доставку структурированных наборов данных. — Использовать внутренние инструменты (Apify, OpenRouter) и собственные методы для ускорения сбора данных, проверки и выполнения задач в соответствии с требованиями. — Обеспечивать надежное извлечение данных с динамических и интерактивных веб-источников, адаптируя подходы для работы с контентом, рендеренным JavaScript, и изменяющимся поведением сайтов. — Поддерживать стандарты качества данных через проверки валидации, контроль согласованности между источниками, соблюдение форматов и систематическую проверку перед доставкой. — Масштабировать операции по сбору данных для больших наборов, используя эффективное пакетирование или параллелизацию, мониторить сбои и поддерживать стабильность при незначительных изменениях структуры сайтов. Требования: — Минимум 3 года релевантного опыта в области инженерии данных, веб-скрапинга, автоматизации или разработки ПО. — Высшее образование (бакалавр или магистр) в инженерии, прикладной математике, компьютерных науках или смежных технических областях является плюсом. — Опыт веб-скрапинга на Python с использованием BeautifulSoup, Selenium или аналогичных инструментов, включая работу с динамическим контентом (JS, AJAX, бесконечная прокрутка) и API через прокси. — Умение извлекать данные из сложных структур (иерархии, архивные страницы, нестабильный HTML). — Опыт очистки, нормализации и валидации данных, предоставление структурированных наборов данных в форматах CSV, JSON, Google Sheets. — Практический опыт работы с большими языковыми моделями (LLM) и AI-фреймворками для улучшения автоматизации и решения задач. — Внимательность к деталям и ответственность за точность данных. — Самостоятельность в работе и умение решать проблемы без постоянного контроля. — Знание английского языка на уровне Upper-intermediate (B2) или выше. Условия: — Частичная занятость, удаленная работа с гибким графиком. — Работа в уникальной гибридной среде, где человеческий опыт и AI-агенты работают совместно. — Возможность участия в бонусных программах, основанных на производительности и качестве работы. Оплата: — До $30 в час в зависимости от уровня и темпа вклада. — Компенсация варьируется в зависимости от проекта, сложности и требуемой экспертизы.

О компании Nexters: международная компания по разработке игр, входящая в холдинг GDEV, с офисами на Кипре, в Армении и Казахстане, а также возможностью удалённой работы. Обязанности: — Проектирование, создание и поддержка масштабируемых конвейеров данных (batch и streaming) — Поддержка и рефакторинг существующих решений — Основная зона ответственности — Bronze и Silver слои платформы данных — Разработка и поддержка моделей data lake / data warehouse — Оптимизация производительности Spark-джобов — Разработка и поддержка dbt моделей и контрактов данных — Внедрение и поддержка мониторинга, наблюдаемости и проверок качества данных — Работа с Kafka-стриминговыми конвейерами — Взаимодействие с командами аналитики, маркетинга, платформы и бэкенда Требования: — Уверенные навыки SQL и Python — Опыт создания ETL/ELT конвейеров в продакшене — Практический опыт работы со Spark/PySpark, особенно в стриминговых сценариях — Понимание распределённых систем обработки и хранения данных — Опыт работы с архитектурами data lake / data warehouse — Практический опыт с Delta таблицами, Parquet и S3 — Опыт работы с оркестраторами, такими как Airflow или аналогичными — Опыт работы с Kafka и стриминговыми конвейерами — Опыт поддержки и улучшения конвейеров данных в продакшене — Опыт с практиками наблюдаемости, мониторинга и качества данных Условия: — Медицинская программа (включая стоматологию) или компенсация, доступная в любом месте работы — Медицинская программа для членов семьи при работе в офисах на Кипре, в Армении и Казахстане — Оплачиваемый отпуск и больничные — Возможность работать в офисах или удалённо — Спортивные активности в офисах (йога, футбол, волейбол) — Пакет поддержки релокации при переезде в официальные локации — Курсы английского языка онлайн, уроки греческого и армянского для сотрудников в соответствующих офисах — Участие в гейм-джемах, хакатонах, конференциях и митапах — Поддержка социальных инициатив и участие в благотворительных проектах Формат работы: в офисах компании на Кипре, в Армении и Казахстане или удалённо.

Задачи: — построение масштабируемых конвейеров данных и инфраструктуры в облачной среде GCP. Требования: — опыт работы Data Engineer от 3 лет; — уверенное владение GCP (BigQuery, Cloud Storage, Dataform); — продвинутые навыки Python и Apache Airflow; — уровень английского B2 и выше. Условия: — долгосрочный контракт с QIC; — полный рабочий день, удалённо, график 5 дней в неделю, с воскресенья по четверг; — отпуск по календарю праздников Катара, 20 рабочих дней отпуска и 10 дней больничных; — оценка эффективности два раза в год; — испытательный срок 3 месяца; — поддержка цифровых кочевников. Преимущества и бонусы: — индивидуальные языковые занятия; — страхование RemoteHealth Premium с покрытием до $5000 в год; — образовательный бюджет; — консультации по налогам с менеджером; — клуб бега QIC в Strava; — корпоративные скидки в программе Privilege, включая Qatar Airways.

Задачи: — Писать кастомные парсеры под соц сети, медиахостинги и маркетплейсы — Реализовывать сервисы на Python — Разрабатывать и поддерживать сервисы для сбора данных Требования: — Опыт разработки и поддержки высоконагруженных парсеров/scrapers от 3х лет — Python от 3х лет — Понимание типов различных прокси-серверов (ротация прокси, мониторинг качества), принципов работы CAPTCHA — Работа с rate limiting — Playwright — AsyncIO, aiohttp/httpx — Опыт reverse engineering внутренних API веб- и мобильных приложений — Опыт работы с базами данных (PostgreSQL / ClickHouse / Redis) — Отличное понимание HTTP/TCP, работы CDN, REST API, GraphQL, антибот-систем и методов их обхода Будет плюсом: — Опыт разработки парсеров для площадок: Instagram, YouTube, Tik Tok, Trip Advisor, Spotify, X, Flickr, LinkedIn, Reddit и аналогичные — Опыт работы с Grafana/Prometheus — Kubernetes — Kafka/RabbitMQ Условия: — Проектная занятость с гибким форматом работы — Прямая коммуникация с техническим руководством без бюрократии — Возможность вырасти в полноценную техническую роль внутри компании

Мы развиваем ИТ-продукт, который включает в себя масштабную WEB-платформу и интерактивного Telegram-бота / Mini App. Наш стек: Python, Airflow 2+, dbt Core, ClickHouse, PostgreSQL, Docker, GitLab. Продукт быстро растет, и сейчас мы ищем Data Engineer, который поможет нам масштабировать аналитическую инфраструктуру и сделать работу с данными быстрой и надежной. Задачи: — Разрабатывать и поддерживать ETL/ELT-процессы на базе Airflow и dbt Core — Интегрировать новые источники данных: PostgreSQL, внешние API и сервисы — Развивать существующее хранилище данных на ClickHouse и поддерживать аналитические витрины — Оптимизировать SQL-запросы и производительность пайплайнов — Поддерживать и развивать инфраструктуру аналитической платформы (Docker, Airflow, dbt) — Взаимодействовать с аналитиками для реализации новых витрин, метрик и источников данных — Участвовать в расследовании инцидентов, связанных с качеством данных и работой пайплайнов — Поддерживать техническую документацию и участвовать в передаче знаний внутри команды Требования: — Опыт на позиции инженера данных от трёх лет — Уверенное знание SQL и опыт работы с аналитическими СУБД — Практический опыт работы с ClickHouse: проектирование таблиц, выбор движков, партиционирование, оптимизация запросов — Опыт разработки и поддержки ETL/ELT-процессов: способность самостоятельно выбирать способ интеграции источника данных, проектировать структуру загрузки, хранение и обновление данных с учетом требований бизнеса и технических ограничений — Опыт работы с Apache Airflow 2+: разработка, поддержка и отладка DAG'ов, понимание механизмов планирования, зависимостей и мониторинга пайплайнов — Опыт работы с dbt Core — Хорошее знание Python: умение разбираться в существующем коде, дорабатывать и разрабатывать новые компоненты интеграций и пайплайнов — Опыт работы с PostgreSQL и понимание особенностей инкрементальных выгрузок данных — Опыт интеграции внешних REST API — Понимание принципов контейнеризации и опыт работы с Docker / Docker Compose — Умение самостоятельно декомпозировать задачи и доводить их до результата Будет плюсом: — Опыт работы с Airflow 3+ — Опыт работы с BigQuery — Опыт работы с Google Analytics / GA4 — Опыт работы с облачными платформами (GCP, Yandex Cloud, AWS) — Понимание базовых принципов работы блокчейн-сетей и транзакционных данных — Опыт использования AI-инструментов для разработки и сопровождения кода Формат работы: удаленно Часовой пояс: MSK

Задачи: — Проектировать схемы данных и писать высоконагруженные SQL-запросы для аналитических и OLTP-задач. — Разрабатывать и поддерживать ETL/ELT-пайплайны, обеспечивая качество и консистентность данных. — Оптимизировать производительность запросов: профилирование, анализ планов выполнения, выбор стратегий партиционирования и шардирования в ClickHouse. — Участвовать в проектировании архитектуры хранения данных, выбирая подходящий движок (MergeTree, ReplacingMergeTree и др.) под конкретную задачу. — Взаимодействовать с бэкенд-командой для интеграции слоя данных с сервисами приложения. — Документировать решения, схемы и подходы для команды. Требования: — Уверенное владение SQL - сложные аналитические запросы, оконные функции, CTE, подзапросы. — Опыт работы с ClickHouse от 3 лет: понимание архитектуры колоночного хранения, движков таблиц, материализованных представлений, настройки репликации и шардирования. — Понимание принципов проектирования схем данных для OLAP-систем. — Опыт работы с большими объёмами данных (десятки и сотни миллиардов строк). — Навыки профилирования и оптимизации запросов. — Знакомство с Linux на уровне уверенного пользователя. Будет плюсом: — Опыт работы с YDB — понимание модели данных, работа с YQL, знание подходов к проектированию распределённых транзакционных схем. — Опыт с другими СУБД: MySQL, MariaDB. — Знакомство с инструментами миграции данных (debezium). — Понимание принципов распределённых систем и CAP-теоремы. Условия: — Полная занятость. — Удаленка, офис\ гибрид (Москва). — Возможность гибкого графика.

Требования: - Высшее техническое образование по статистике/прикладной математике/IT - Знания Python, Docker, n8n, SQL Обязанности: - Создание LLM-агентов на основе метаданных DWH - Покрытие витрин дополнительными тестами и автоматизация проверок данных - Разбор структуры DWH и изучение документации Условия: - Официальное трудоустройство на 3-6 месяцев - Заработная плата 50 000 руб. гросс - Частичная занятость удаленно, гибкий график работы

Location: Toronto; Europe; London; New York Workplace: Remote WHO ARE WE? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Role Overview: As a Senior Member of Technical Staff specializing in web data for pre-training, you will play a pivotal role in developing the large scale web data pipeline that underpins Cohere’s advanced language models. In this role, you will work extensively with large-scale web corpora, transforming raw, noisy internet data into high-quality training data for pretraining. You will own key components of the data pipeline, including extraction, parsing, deduplication, and filtering. You will also analyze the composition and quality of web data, study its impact on downstream model performance, and collaborate closely with the broader data and evaluation teams to iterate on the training corpus. Your work will be essential to Cohere’s mission of delivering efficient and reliable language understanding and generation capabilities, driving innovation in natural language processing. If you are passionate about transforming data into the foundation of AI systems, this role offers a unique opportunity to make a meaningful impact. Please Note: We have offices in London, Paris, Toronto, San Francisco and New York but also embrace being remote-friendly! There are no restrictions on where you can be located for this role. (EST/EU) Key Responsibilities: - Maintain large-scale pipelines for processing web corpora. - Work on filtering and quality-scoring systems to identify high-value web documents. - Analyze web data composition across domains, languages and time periods. - Develop and maintain highly-performant deduplication pipelines. - Collaborate with cross-functional teams, including researchers and engineers, to ensure data pipelines meet the demands of cutting-edge language models. Qualifications: - Strong software engineering skills, with proficiency in Python and experience building data pipelines. - Familiarity with data processing frameworks such as Apache Spark, Apache Beam, Pandas, or similar tools. - Experience working with large-scale web datasets. - Knowledge of data quality assessment techniques and experimentation with data mixtures. - A passion for bridging research and engineering to solve complex data-related challenges in AI model training. Bonus: paper at top-tier venues (such as NeurIPS, ICML, ICLR, AIStats, MLSys, JMLR, AAAI, Nature, COLING, ACL, EMNLP). If some of the above doesn’t line up perfectly with your experience, we still encourage you to apply! Working Location: This role can be based remotely or from one of our office locations listed on the job description - there is no minimum in-office qualification requirement. We care most about hiring exceptional people regardless of locations, though please check the location listed on the posting for guidance around the core time zone or working hours alignment expected for the role.   FULL-TIME EMPLOYEES AT COHERE ENJOY THESE PERKS: - A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch. - Full health and dental benefits, including a separate budget for mental health. - RRSP matching, 401K, Pension Scheme. - 100% Parental Leave top-up for up to 6 months, for either parent. - Annual enrichment benefits: Arts & culture, fitness/wellness, quality time, and a workspace improvement credit. Education & learning stipend for conferences, courses, and coaching. - 6 weeks of paid vacation (30 working days!) - Budget for traveling to other offices if you are remote, plus an annual company offsite. HOW AND WHERE WE WORK: - Cohere is remote-friendly, but we also have offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul with more opening soon. - For those in the office: a daily lunch program, plenty of snacks, and regular community and social events. - For those not near an office: a co-working benefit so you can work alongside others in your city. - Everyone receives a $500 home office stipend to set up your workspace properly. If any of the above doesn’t line up exactly with your experience, we still encourage you to apply. We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form https://docs.google.com/forms/d/12a6IrLdF3kI2nonKSr4tiFuz18rLQbaeYV-JM9L4o9Q/edit, and we will work together to meet your needs. We may use AI-enabled tools to screen and assess applicants against the criteria for this position. This helps our recruiters identify potentially qualified candidates, but it doesn't limit the applications our recruiters may review or consider. Beware of Scams: Cohere will never ask for payment or third-party services (e.g., CV writing) as part of our hiring process. All legitimate roles are listed on the Cohere careers page and LinkedIn only, with all communications from Cohere employees coming from an @cohere.com or @cw.cohere email alias. If jobs are viewed on other sites then please verify these through our official careers https://cohere.com/careers page.