Intermediate Data Engineer (Python)
GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. Mo…
GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster.
The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software.
*Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab.
An overview of this role
As a Data Engineer at GitLab, you'll build, run, and maintain the pipelines and infrastructure that bring data from our enterprise applications, internal systems, and GitLab's software as a service (SaaS) platform into our data platform. Your work will power company reporting, data science, and artificial intelligence (AI) self-service.
You'll own features end to end, from source integration to tested, monitored, documented data ready for use. You'll focus on keeping the platform reliable, secure, and cost-efficient as data volumes and AI usage grow, and making governed data ready for AI tools, agents, and Model Context Protocol (MCP)-based access. This is an AI-first role: you'll use AI coding agents every day while maintaining high standards for code review and testing. You'll report to the Director, Data Platform.
What you’ll do
- Build, extend, and maintain extract, load, transform (ELT) pipelines from enterprise applications such as Salesforce, Zuora, Zendesk, and Marketo, internal systems, and the GitLab SaaS platform. Integrate data through application programming interfaces (APIs) and event sources such as Snowplow.
- Manage and maintain the infrastructure behind the platform, including SaaS tools such as Snowflake, Fivetran, and Monte Carlo, and self-managed services such as Airflow and Kubernetes.
- Write maintainable, well-tested, performant Python and Structured Query Language (SQL) code that meets our internal standards, and review GitLab team members' merge requests.
- Own day-to-day platform operations: triage and resolve failed pipelines, infrastructure issues, and GitLab team member requests. Use monitoring, service-level agreements (SLAs), data contracts, query and warehouse tuning, and data quality checks to keep the platform reliable and cost-efficient.
- Partner with Analytics Engineers, Data Analysts, Data Scientists, Data Governance, and business partners so data lands in the shape they need for downstream use.
- Use AI coding agents such as Claude Code, OpenCode, GitLab Duo, or similar tools to speed up development, debugging, testing, and documentation without compromising review rigor.
- Apply role-based access control (RBAC) and masking policies to make governed data available to AI tools and agents. Handle data subject to Sarbanes-Oxley (SOX) controls, material nonpublic information (MNPI), and personal data safely.
- Ship medium-sized features independently in small, iterative merge requests, and document architecture and runbooks in the handbook.
What You'll Bring
- Professional experience shipping production data pipelines in Python.
- Deep, hands-on SQL skills and experience with a cloud data warehouse; Snowflake is preferred.
- Hands-on experience with ELT or extract, transform, load (ETL) pipelines at scale, including incremental loads, schema changes, backfills, and API ingestion.
- Experience with orchestration tools such as Airflow, familiarity with dbt, and solid data modeling fundamentals, including Kimball dimensional modeling.
- Experience running and troubleshooting data infrastructure, with comfort using Git-based workflows, continuous integration and continuous delivery (CI/CD), and code review.
- Hands-on experience using AI coding tools in your daily work, with an understanding of where they help and where they fall short.
- Clear written communication, the ability to use GitLab, and comfort working asynchronously in a fully remote organization in line with GitLab's values.
- Preferred, not required: experience with Snowflake; Docker, Kubernetes, Google Cloud Platform (GCP), or Terraform; SaaS application APIs such as Salesforce, Zuora, or Zendesk; data observability tooling; or Snowflake administration.
About the team
The Data Platform team within Enterprise Data is responsible for a scalable, reliable and secure data platform for data analytics at GitLab. That includes managing and maintaining the infrastructure we use, both SaaS and self-managed, and building and maintaining the data pipelines that bring data into it. We make data reliable, secure and ready for analytics, data science and AI tools. As more questions go straight to AI tools and agents, the platform underneath has to be more reliable, better governed and more cost-efficient than ever. We're fully remote and globally distributed, and work asynchronously through issues, merge requests and the handbook.
How GitLab Supports Full-Time Employees
- Benefits to support your health, finances, and well-being
- Flexible Paid Time Off
- Team Member Resource Groups
- Equity Compensation & Employee Stock Purchase Plan
- Growth and Development Fund
- Parental Leave
Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement. Additionally, studies have shown that people from underrepresented groups are less likely to apply to a job unless they meet every single qualification. If you're excited about this role, please apply and allow our recruiters to assess your application.
Country Hiring Guidelines: GitLab hires new team members in countries around the world. All of our roles are remote, however some roles may carry specific location-based eligibility requirements. Our Talent Acquisition team can help answer any questions about location after starting the recruiting process.
Privacy Policy: Please review our Recruitment Privacy Policy. Your privacy is important to us.
GitLab is proud to be an equal opportunity workplace and is an affirmative action employer. GitLab’s policies and practices relating to recruitment, employment, career development and advancement, promotion, and retirement are based solely on merit, regardless of race, color, religion, ancestry, sex (including pregnancy, lactation, sexual orientation, gender identity, or gender expression), national origin, age, citizenship, marital status, mental or physical disability, genetic information (including family medical history), discharge status from the military, protected veteran status (which includes disabled veterans, recently separated veterans, active duty wartime or campaign badge veterans, and Armed Forces service medal veterans), or any other basis protected by law. GitLab will not tolerate discrimination or harassment based on any of these characteristics. See also GitLab’s EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know during the recruiting process.
Похожие вакансии Data Engineer
Andersen is hiring a Data Engineer for a project automating revenue reconciliation processes and enhancing financial data accuracy, transparency, and operational efficiency.
The customer is a global investment management firm providing tailored investment solutions to institutional and private clients. It combines financial expertise with long-term investment strategies to help clients achieve their objectives while adapting to changing market conditions. The organization focuses on innovation, responsible investing, and operational excellence, continuously enhancing its capabilities to deliver sustainable value and support long-term growth.
The project is focused on automating revenue reconciliation processes within a large-scale data platform initiative. It aims to ensure accurate and efficient reconciliation of payments received through external payment providers, including driver and courier balance top-ups and commission deductions, while improving financial data accuracy, transparency, and operational efficiency.
- Gathering and clarifying requirements from marketing analysts and BI, and turning them into changes to attribution logic, sources and data marts.
- Leading the migration of the attribution pipeline (dbt models, Airflow DAGs, sources) to the core DWH platform. Running old and new in parallel and reconcile them before switching over.
- Building and optimizing incremental data marts in BigQuery (dbt/SQL) and Airflow, including recalculations and backfills.
- Designing and implementing data quality checks and investigating discrepancies against product and source data.
- Performing deep-dive analysis of external inputs (AppsFlyer, ad network cost feeds, Google Sheets dictionaries, upstream marts) to keep data flowing reliably from source to consumption.
- Participating in testing, data validation and release processes, including repointing downstream consumers.
- Maintaining technical documentation for the domain and spreading knowledge across the team.
- Experience as an Analytics Engineer / DWH Engineer / Data Analyst working with DWH for 3+ years.
- Hands-on experience with data warehouses.
- Strong understanding of DWH architecture and data layers (ODS, Data Marts).
- Understanding of incremental loads, historical data handling and deduplication.
- Expert SQL skills.
- Experience designing and optimizing data marts.
- Experience with BigQuery or similar columnar DB.
- Experience with dbt on large incremental projects.
- Experience migrating pipelines between platforms with data parity validation.
- Experience with Airflow or a similar orchestration tool.
- Knowledge of Python for data processing and ETL tasks.
- Ability to work with stakeholders and translate business needs into data requirements.
- Level of English – from Intermediate+ or above.
- Experience working with mobile and marketing attribution data using AppsFlyer, Adjust, or Branch, including ad network cost data, SKAN, and ATT.
- Familiarity with ad fraud detection and analytics, particularly AppsFlyer Protect360.
- Experience with GCP, including Cloud Storage and Dataform.
- Experience with analytics and BI tools such as Lightdash, Looker, and Tableau.
- Experience in teamwork with leaders in FinTech, Healthcare, Retail, Telecom, and others. Andersen cooperates with such businesses as Samsung, Siemens, Johnson & Johnson, BNP Paribas, Ryanair, Mercedes, TUI, Verivox, Allianz, T-Systems, etc..
- The opportunity to change the project and/or develop expertise in an interesting business domain.
- Job conditions – you can work both fully remotely and from the office or can choose a hybrid variant.
- Guarantee of professional, financial, and career growth! The company has introduced systems of mentoring and adaptation for each new employee.
- The opportunity to earn up to an additional 1,000 EUR per month, depending on the level of expertise, which will be included in the annual bonus, by participating in the company's activities.
- Access to the corporate training portal, where the entire knowledge base of the company is collected and which is constantly updated.
- Bright corporate life (parties / pizza days / PlayStation / fruits / coffee / snacks / movies).
- Certification compensation (AWS, PMP, etc).
- Referral program.
- Private health insurance and sports compensation, depending on the type of employment.
Join us!
В Финтехе мы создаём платёжные продукты, сервисы и инструменты для управления личными финансами. Финтех уже превратился из стартапа в полноценный и крайне важный продукт.
Сейчас мы собираем новое — аналитическое — хранилище данных и формируем для этого команду. В хранилище нет легаси, поэтому у вас будет возможность оказаться у истоков нового проекта. У нас большие планы — приходите помогать их реализовать!
Строить ETL-процессы
Нужно извлекать из источников ценную информацию, обрабатывать её и представлять в виде, удобном для аналитиков. Сложность пайплайнов может варьироваться от простых инжестов данных из внешней среды и приземления результата в хранилище до витиеватых графов со множеством инпутов и аутпутов.
Оптимизировать логику
Как известно, у нас нет собственного легаси, но мы часто работаем с прототипами, реализованными аналитиками, чьё представление о прекрасном может не совпадать с нашим. Поэтому мы распутываем нетривиальную логику и предлагаем более строгую, консистентную и оптимальную, ориентируясь на большие объёмы данных.
Обеспечивать доступность сервиса
Олимпиадный подход подразумевает одноразовость результата. Мы преследуем совсем другие цели, поэтому максима «сработало раз — и ладно» не о нас. Идеал такой: хранилище обеспечивает регулярную поставку с минимально возможным количеством сбоев, а данные — корректны, полны, непротиворечивы и отвечают самым строгим стандартам качества.
Проектировать структуру хранилища
Лучшее аналитическое DWH ещё только в планах. Мы хотим заложить основы, которые позволят ему быстро развиваться, обрастая при этом не «костылями» и «велосипедами», а современными инженерными практиками и подходами.
Взаимодействовать с аналитиками
Они наши главные и любимые заказчики. Вместе с ними вам предстоит обдумывать бизнес-задачи, иногда вы будете спорить, но в итоге — находить оптимальные решения. Неотъемлемая часть этого процесса — накапливание личного опыта в бизнес-домене Финтеха. Прибавим сюда рост инженерного опыта — и получим двойную выгоду. Почти как у Яндекс Пэй.
* Уверенно программируете на Python
* Хорошо знаете SQL и понимаете устройство современных СУБД
* Понимаете и можете объяснить необходимость непрерывной интеграции и доставки (CI/CD), преимущества использования систем контроля версий, код-ревью, тестирования и мониторинга
* Работаете в командной строке Unix-подобных систем (Linux, macOS)
* Создавали сложные высоконагруженные ETL- и ELT-процессы (Hadoop, Hive, Spark)
* Разрабатывали процессы обработки событий, приближенные к реальному времени (Kafka, Spark Streaming, Flink)
* Участвовали в проектировании хранилищ данных
* Работали по методологии Agile
* Руководили стажёрами или начинающими специалистами
* Знакомы со специализированными аналитическими БД (Greenplum, Vertica, ClickHouse, Teradata) и аналитическими инструментами на базе Python (Jupyter, pandas, SciPy, Matplotlib)
* Публикуете свой код в доступном репозитории, пишете статьи, выступаете на конференциях
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