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‑frie...