Senior Data Engineer: Data Warehouse & Reporting

Responsibilities

• Own ingestion pipelines from source systems into the warehouse: extraction, staging, transformation, publication.
• Write and optimize substantial SQL; transformation logic and reporting-layer performance both live there.
• Build and maintain Airflow DAGs with sound scheduling, dependencies, retries, idempotency, and backfill behavior.
• Integrate new sources, internal and third-party.
• Guarantee data quality — reconciliation against sources, validation, and alerting when a load is wrong, not just when it fails.
• Work with the BI team on the semantic layer and report performance; design upstream models that make reporting straightforward.
• Improve pipeline maintainability, testing, and observability.
• Set data engineering standards and mentor others.
• Practical data quality discipline — reconciliation, validation, pipelines that fail loudly rather than quietly.

Preferred

• dbt or a comparable transformation framework.
• Data quality tooling — Great Expectations, Soda, or similar.
• Spark, or columnar and MPP warehouses.
• Financial and operational reporting domains

Work schedule

Monday - Friday from 14:00 till 22:00

Key Responsibilities

Design the warehouse model — layering, grain, incremental load strategy, historization, change handling.

Skills, Knowledge and Expertise

• 6+ years in data engineering, or backend engineering with a heavy data focus.
• Strong Python, and production Airflow experience — DAG design, scheduling semantics, idempotent tasks, backfills, debugging live failures.
• Expert SQL on PostgreSQL: complex analytical queries, execution plans, and knowing why something is slow.
• Solid warehouse modeling — dimensional design, slowly changing dimensions, grain, and incremental patterns.
• Experience building incremental ingestion from REST APIs: pagination, rate limits, retries, late-arriving data.
• Working knowledge of Power BI and the Microsoft BI stack; enough to model for it and to debug a slow or incorrect report.
• Containers and CI/CD; able to own your pipelines in production.
• Experience shipping production code with AI coding tools, and the critical eye that comes with it.
• Fluent Russian — engineering collaboration happens in Russian.
• Professional English for documentation.
• Clear technical writing; you can document a model so an analyst uses it without asking you.

Competitive salary package;

• Being part of a international, dynamic work environment;
• Professional development (seminars, courses);
• Paid time off (PTO) such as sick days and vacation days;