Staff Data Platform Engineer

Responsibilities

• Define and evolve data contract standards, including schema enforcement, versioning, and validation patterns.
• Design interoperable ingestion and publishing frameworks for upstream producers to integrate with the data platform.
• Build and standardize APIs, libraries, or SDKs for event logging, schema validation, and contract compliance.
• Establish best practices for schema registry usage and distributed schema validation across streaming and batch systems (e.g., Kafka).
• Design patterns for data lake vs. warehouse usage, curated layers exposure, and safe downstream access.
• Lead reverse ETL and activation architecture for operational use cases.
• Define and enforce access control, governance, and compliance standards (e.g., PHI/PII handling, DEID boundaries, RBAC).
• Partner with Product Engineering, Security, Compliance, Analytics Engineering, and Infrastructure teams.
• Mentor engineers and influence engineering culture around data quality, ownership, and contracts.
• Drive adoption of AI-assisted development practices and design guardrails for AI access to data systems.

Requirements

• 8+ years of experience in data engineering, platform engineering, or backend platform development.
• Demonstrated experience designing data contracts, schema governance, or producer/consumer standards at scale.
• Strong expertise in Python and SQL, with hands-on experience building scalable data frameworks.
• Experience with distributed data systems such as Spark (Databricks or EMR) and modern lakehouse architectures (Delta Lake / Iceberg).
• Experience with data warehouses such as Snowflake and strong understanding of performance and access patterns.
• Familiarity with schema registry systems and schema evolution in streaming systems (e.g., Kafka).
• Experience building APIs, shared libraries, or platform services adopted by multiple teams.
• Strong understanding of access control, RBAC, and compliance constraints in regulated environments.
• Proven ability to lead cross-functional architectural initiatives.
• Clear communication skills and track record of influencing standards.
• Experience with AI-assisted development tools or cloud-based coding environments.
• Strong understanding of governance for GenAI systems.

Nice-to-haves

• Experience designing reverse ETL frameworks or operational activation pipelines.
• Familiarity with metadata and governance platforms (e.g., Unity Catalog, Collibra, OpenMetadata).
• Experience building internal developer platforms for event logging or data publishing.
• Experience in regulated environments involving PHI/PII.
• Experience integrating streaming systems (Kafka/Kinesis) with warehouse and lakehouse ecosystems.