Role Summary
The owner of the POD’s data backbone — the Atlas Data Lake, the Graph Database that underpins the Ontology, and the governance that keeps FedEx, customer and third-party data trustworthy. You make sure the intelligence layer is fed with clean, governed, well-modelled data.
Key Responsibilities
- Design and build ingestion and transformation pipelines over the Atlas Data Lake and Databricks for FedEx, customer and third-party data.
- Model and maintain the Graph Database supporting the Ontology Graph in partnership with the AI Engineer.
- Implement data governance — lineage, cataloguing, quality, access control and PII handling — aligned to central platform standards.
- Build streaming and batch pipelines (Azure Event Hub, Spark) and serve data to services via Postgres / SQL / Redis.
- Set data-quality SLAs and monitoring; mentor the Junior Data Engineer.
Must-have Skills & Technologies
- Lakehouse engineering on Databricks and a data lake (Atlas Data Lake or Delta Lake / equivalent).
- Strong SQL and Python; Spark (batch and structured streaming).
- Graph databases (Neo4j, Cosmos DB Gremlin or similar) and graph data modelling.
- Data governance and lineage tooling (e.g. Unity Catalog, Purview, Collibra) and data quality frameworks.
- Postgres, Redis, and event streaming (Azure Event Hub / Kafka).
Prior Experience
- 7+ years in data engineering, including lakehouse and streaming pipelines in production.
- Demonstrable ownership of data governance / lineage in a regulated or large-scale data environment.
Nice to Have
- Feature engineering for ML/AI workloads; serving data to LLM/RAG systems.
- Logistics / supply-chain data domains.
