Lead Data Engineer – Architecture

Lead R&D for data engineering accelerators by evaluating, improving, and delivering client demos of new capabilities across the data engineering lifecycle.

Provide hands-on technical leadership for client data programs including solution design, architecture review, client communication, and delivery risk management.

Translate complex data engineering challenges into clear requirements and validation criteria for AI engineers, guiding technical improvements and ensuring high-quality outputs.

Hands-on experience with full data engineering stack including source integration, storage, transformation, modeling, reporting, and operations on at least one modern data platform (e.g., Microsoft Fabric, Databricks, Snowflake).

Strong database fundamentals and hands-on development skills including advanced SQL, stored procedures, indexing, and performance tuning across relational and NoSQL systems.

Proven end-to-end delivery experience across greenfield and brownfield data platforms, covering requirements, architecture, deployment, and operations.

Strong Python skills for data processing, integration, and automation; ability to translate business requirements into practical technical designs. Work Experience Required: Not explicitly mentioned in the JD.

Experienced leader capable of managing both R&D accelerator initiatives and client-facing data engineering programs with strong ownership and problem-solving.

Technically proficient across a wide range of data engineering domains and tools, able to validate AI-generated outputs and improve engineering standards.

Comfortable engaging in client conversations to clarify technical requirements, propose solutions, and manage delivery risks effectively.