Lead Data Engineer - NBA
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The Lead Data Engineer owns the NBA platform's data foundation on the Databricks Lakehouse. This role is responsible for the architecture, delivery, quality, governance, and operational reliability of the data products that power decision intelligence, machine learning, reinforcement learning, agentic AI, and real-time decisioning across the platform. You lead a small team of engineers and contractors while remaining deeply hands-on, building and evolving the pipelines, feature layers, streaming architectures, and data quality controls that turn raw healthcare data into trusted, production-ready assets consumed across the NBA ecosystem.
Key Responsibilities
•Pod delivery — Own end-to-end delivery for the data engineering pod, including planning, execution, quality, and operational readiness.
•Lakehouse architecture — Own the design and evolution of the NBA Databricks Lakehouse, including Bronze, Silver, and Gold layer standards, data lineage, and governance patterns.
•Data pipeline engineering — Design, build, and maintain scalable batch and real-time pipelines that process member, clinical, claims, behavioral, engagement, and socioeconomic datasets.
•Feature platform ownership — Lead the design and operation of Gold-layer feature tables and reusable data products that support model training, scoring, reinforcement learning, and decision intelligence workloads.
•Streaming and event architecture — Design and implement Kafka- and Spark Structured Streaming-based ingestion and processing patterns that enable near real-time decision-making.
•Data quality and observability — Establish platform-wide standards for data validation, monitoring, lineage, reconciliation, alerting, and operational visibility; treat data quality issues as production incidents.
•Performance and optimization — Drive optimization of Spark workloads, Delta Lake storage patterns, partitioning strategies, and query performance to support enterprise-scale volumes efficiently.
•Data governance — Ensure compliance with PHI, HIPAA, auditability, retention, and governance requirements through Unity Catalog, lineage tooling, and secure data management practices.
•Team leadership — Lead and mentor engineers and contractors; conduct code reviews, establish engineering standards, and drive adoption of best practices across the pod.
•Cross-team coordination — Partner with Decision Intelligence, Data Science, AI Engineering, and Platform Engineering teams to ensure reliable, governed, and performant data delivery throughout the NBA ecosystem.
Use your skills to make an impact
Required Qualifications
• Bachelor's degree in computer science or related field
• 7+ years of data engineering experience with at least 1–2 years in a lead engineer or technical leadership capacity.
• Expert-level SQL and Python skills with significant experience developing and operating Spark-based data platforms.
• Deep experience with Databricks, Delta Lake, and modern Lakehouse architectures.
• Experience designing and operating Medallion Architecture (Bronze/Silver/Gold) implementations at enterprise scale.
• Experience building and supporting both batch and streaming data pipelines in production environments.
• Strong understanding of data quality engineering, validation frameworks, lineage, monitoring, and operational support models.
• Experience designing secure and compliant data platforms in regulated environments.
• Demonstrated ability to lead a small engineering team while remaining an active hands-on contributor.
• Strong communication skills and the ability to explain technical tradeoffs and architecture decisions to engineering, business, and compliance stakeholders.
Preferred Qualifications
• Deep experience with Databricks Feature Store, Delta Live Tables, Unity Catalog, and Databricks Workflows.
• Experience supporting machine learning, reinforcement learning, recommendation engines, or dec