Principal Data Engineer

Responsibilities

• Own the design, build and operation of the data lake and ingestion platform end-to-end, from architecture through production reliability.
• Build low-latency batch and streaming pipelines that ingest signals from internal and external sources, normalize them to a common schema, enrich them with context and serve model-ready data to the layers above.
• Make adding a new data source a routine task rather than a project, so our view of risk keeps widening over time.
• Establish data quality, freshness, completeness, lineage and observability so the platform is trustworthy enough to automate on top of.
• Build data pipelines that ground generative AI, including unstructured text and threat intelligence processing, embedding generation, vector storage and retrieval.
• Own deployment, CI/CD and operational reliability of the platform on Kubernetes.
• Partner with data science, product and architecture to turn the platform into a shared foundation across 360 Fraud Protection.

• Extensive experience building and operating large-scale data platforms and data lakes, with comfort working at high data volumes.
• Deep, hands‑on expertise with Apache Spark, Apache Flink and modern big‑data systems.
• Proven command of best practices for building and maintaining data pipelines in both batch and streaming modes.
• Strong production engineering skills across the full delivery lifecycle, including Kubernetes and CI/CD tooling, with the ability to ship end‑to‑end.
• A track record of owning data infrastructure end‑to‑end with limited supervision.