Senior data engineer

This is a hands-on building role: you turn raw, messy fabrication data into the clean, well-modeled, AI-ready datasets that our AI/ML and analytics workloads run on 🚀

🧑🏻‍💻 Responsibilities:

Build and operate ingestion, ELT/ETL, and orchestration pipelines that move data from our MongoDB Atlas operational store and other sources into our analytical and AI-serving layers

Implement layered (medallion-style) transformations with idempotent, backfillable, incrementally loaded jobs

Apply deduplication, normalization, and validation so downstream data is high-quality and trustworthy

Modernize legacy / homegrown data flows via incremental, strangler-fig migrations that keep production stable

Build embeddings and vector pipelines, and the feature/retrieval-ready datasets that RAG, semantic search, and agentic workloads depend on

Make production data AI-ready in practice: well-structured, lineage-tracked, and retrieval-friendly, in partnership with ML and application engineering

Implement real-time and change-data-capture flows from MongoDB (Change Streams / CDC) where workloads require fresh data

Implement the canonical data model, schemas, and data contracts defined by the Data Architect — enforced in-repo so other teams build against stable definitions

Exercise sound persistence judgment in execution: land data in the right store (document / NoSQL, vector, analytical) per the architectural direction

Contribute to build-vs-buy decisions by prototyping with proven, industry-standard tooling over custom development

Establish testing, data-quality, and lineage checks for the pipelines you own, with clear alerting and runbooks

Instrument pipeline observability (freshness, volume, schema-drift, cost) so failures are caught before consumers feel them

Use AI-assisted development tools (Claude Code, Copilot, Cursor) as a force multiplier for transformation logic, query tuning, and migration scripting

Partner with database engineering on extracting from and pro