• Defining and evolving data contract standards across the company, including schema enforcement, versioning, and validation patterns. • Designing interoperable ingestion and publishing frameworks that enable upstream producers (e.g., product engineering teams) to integrate seamlessly with the data platform. • Building and standardizing APIs, libraries, or SDKs that simplify event logging, schema validation, and contract compliance. • Establishing best practices for schema registry usage and distributed schema validation across streaming and batch systems (e.g., Kafka-based systems). • Designing clear patterns for: • When to use the data lake vs. the warehouse • How curated layers are exposed • How downstream consumers access data safely • Leading reverse ETL and activation architecture to support operational use cases. • Defining and enforcing access control, governance, and compliance standards (e.g., PHI/PII handling, DEID boundaries, RBAC). • Partnering with Product Engineering, Security, Compliance, Analytics Engineering, and Infrastructure to align on standards and long-term direction. • Mentoring engineers and influencing engineering culture around data quality, ownership, and contracts. • Driving adoption of AI-assisted development practices (e.g., cloud-based coding environments, internal AI tooling, or agentic workflows) to accelerate platform delivery. • Designing guardrails for AI access to data systems, including scoped permissions, auditing, and compliance-aware controls. • Partnering with product and AI teams to ensure our data contracts, schemas, and curated layers are AI-consumable and safe by default. • Evaluating how internal data platform assets can power AI use cases and intelligent automation across the company.