- Owning the design, build, and operation of production-grade data platforms and pipelines across cloud, hybrid, and on-premise environments.

- Leading end-to-end data engineering delivery across ingestion, transformation, data modeling, orchestration, serving and productionization for analytics and AI use cases.

- Architecting scalable batch, streaming, real-time, and API-driven data solutions that are reliable, maintainable, and fit for business-critical decisioning.

- Designing data foundations that are trusted and reusable with strong practices across data quality, governance, cataloging, lineage, and access control.

- Leading GenAI-enabled implementations with strong architectural judgment and hands-on credibility across RAG, retrieval design, LLM application patterns, workflow design, evaluation and production deployment.

- Writing, reviewing and raising the bar on engineering work in Python, Java, or Scala, with strong depth in distributed data systems and modern data platforms.

- Building reusable data engineering assets and implementation patterns that accelerate delivery across client engagements.

- Translating ambiguous business problems into scalable data and AI engineering solutions in partnership with senior clients.