We're looking for a Lead Data Engineer (Azure, DWH) to join a long-term project in the insurance domain. You will take ownership of a strategic initiative — building a modern cloud-based data warehouse that will replace legacy reporting and month-end processes. This role combines hands-on engineering, technical leadership, and direct collaboration with the client.

Major responsibilities:

— Act as a primary technical point of contact for the client, clarifying requirements and translating business goals into technical solutions

— Own and drive the DWH delivery plan, including prioritization, planning, and progress tracking

— Identify risks and dependencies (data, legacy systems, delivery) and proactively propose mitigation strategies

— Perform code reviews and ensure high quality of SQL, PySpark, and data models across the team, ensure consistent quality and architectural alignment

— Align technical solutions with stakeholders and present trade-offs and recommendations

— Distribute tasks within the team and maintain a sustainable development pace.

We'd love to hear from you if you have:

— 5+ years of experience in Data Engineering, including 2+ years in Azure

— Expert-level SQL skills (complex analytical queries, performance optimization)

— Strong experience with Azure Synapse Analytics, PySpark

— Hands-on experience with Azure Data Lake Storage Gen2 and data layer design (Raw / Silver / Gold)

— Experience with Azure SQL Managed Instance

— Strong knowledge of data modeling (Kimball / Inmon, SCD, historical data handling)

— Solid experience designing and building enterprise Data Warehouses (fact/dimension modeling, aggregation layers)

— Experience in technical leadership (code reviews, architecture decisions, mentoring)

— Experience working in Scrum teams and managing delivery from high-level requirements

— English level B2+ (regular communication with the client).

Nice to have:

— Experience with Python and Azure Data Factory

— Background in insurance domain (premiums, commissions, taxes, etc.)

— Familiarity with BI tools (e.g., Power BI)

— Experience with lift-and-shift migration from legacy systems

— Azure certifications (e.g., DP-203).