- Own the end-to-end data platform for AI research, from continuous ingestion and preprocessing through governance and export.
- Scale the Databricks lakehouse and distributed processing stack, making architecture and build-versus-buy decisions.
- Build reusable infrastructure for in-house synthetic data generation across modalities.
- Implement dataset versioning, lineage, metadata, quality checks, and stable train/evaluation splits.
- Own pipeline reliability, observability, and processing cost per dataset.
- Partner with ML researchers to turn data needs into self-serve tools.
- Mentor engineers and improve data engineering practices through code review, testing, documentation, and runbooks.
- Work with Security and compliance stakeholders on data handling, licensing, and access controls.
- Shape the long-term data platform roadmap with engineering and research leadership.
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