Senior AI Data Engineer - India

Description and Requirements

• Architecture for Scale (Primary Focus) — Design the reference architecture and delivery patterns that take governed data from the semantic layer to every downstream consumer — dashboards, applications, and AI agents — reliably and at scale, with the build-vs-buy calls and standards that let the platform grow without painful rework.
• AI Data Products & Agents — Architect and build the Data Agent, the natural-language interface that lets business users query governed data without writing SQL; design agentic workflows with orchestration, tool/function calling, retrieval, evaluation, and guardrails.
• Semantic Foundation — Co-design the semantic layer (HoneyDew / DBT) — the metrics, dimensions, and governed business meaning that power both analytics and AI — ensuring agents query governed definitions, not raw tables.
• Technical Leadership — Serve as technical lead and trusted advisor; set patterns, run design reviews, and mentor engineers, partnering with the Data Engineer to take designs to production.
• Business Translation — Translate ambiguous business problems into clear technical requirements; own stakeholder relationships and move fluently between executive framing and engineering detail.
• Ownership & Execution — Take features and subsystems end-to-end, proactively identifying gaps and driving work forward even when requirements are incomplete or evolving.
• 8+ years across data architecture and hands-on engineering, including production systems that held up under real enterprise use.
• Strong proficiency in Python and SQL, with deep, hands-on experience designing and building on Snowflake and DBT.
• Proven experience designing and building LLM / agentic solutions — RAG, function / tool calling, orchestration (LangChain / LlamaIndex / LangGraph), prompt engineering, and evaluation / observability.
• Deep understanding of the semantic layer as the architectural bridge between source data and business meaning — and why it is foundational to trustworthy AI.
• Demonstrated ability to translate complex technical concepts for non-technical stakeholders and influence architecture decisions across teams.
• Strong communication and the ability to lead through execution in a fast-moving environment.