Senior Data Engineer - NBA
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The Senior Data Engineer builds and evolves the data foundation that powers the NBA platform on the Databricks Lakehouse. This role is responsible for developing scalable data pipelines, feature engineering workflows, and real-time ingestion patterns that transform raw healthcare, member, behavioral, engagement, and socioeconomic data into trusted assets for decision intelligence, machine learning, reinforcement learning, and AI workloads. This is a hands-on engineering role focused on delivering reliable, well-governed, and high-quality data products while partnering closely with Data Science, AI Engineering, and Platform Engineering teams.
Key Responsibilities
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Data pipeline development — Design, build, and maintain Bronze, Silver, and Gold lakehouse pipelines that power member profiles, clinical signals, engagement data, and decision intelligence use cases.
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Feature engineering — Develop and maintain Gold-layer feature tables and reusable data products that support model training, model scoring, reinforcement learning, and real-time decisioning.
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Batch and streaming ingestion — Build and support batch and real-time ingestion pipelines using Databricks, Spark, Delta Lake, and event-driven data architectures.
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Data quality engineering — Implement data quality validation, reconciliation, monitoring, alerting, and testing controls to ensure trusted and production-ready data assets.
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Data modeling — Design scalable data models that balance usability, governance, performance, and long-term maintainability.
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Performance optimization — Tune Spark workloads, storage strategies, partitioning schemes, and query performance to improve efficiency, scalability, and cost management.
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Data governance — Follow established standards for data lineage, security, PHI handling, auditability, and regulatory compliance.
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Platform integration — Partner with Decision Intelligence, AI Engineering, and Platform Engineering teams to ensure high-quality data is available across the NBA ecosystem.
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Operational support — Diagnose and resolve pipeline failures, data quality issues, and production incidents to maintain reliable platform operations.
Use your skills to make an impact
Required Qualifications
- 5+ years of data engineering experience building and operating production data platforms.
- Strong SQL and Python skills with hands-on experience developing Spark-based data pipelines.
- Experience with Databricks, Delta Lake, or comparable lakehouse platforms.
- Experience implementing Medallion Architecture (Bronze/Silver/Gold) data patterns.
- Experience building batch and streaming data processing pipelines.
- Strong understanding of data modeling, pipeline testing, data quality controls, and operational support practices.
- Familiarity with modern cloud-based data platforms and distributed data processing systems.
- Strong communication skills and the ability to collaborate across engineering, analytics, and business teams.
Preferred Qualifications
- Experience with Databricks Feature Store, Unity Catalog, Delta Live Tables, and Databricks Workflows.
- Experience with Spark Structured Streaming and real-time feature engineering.
- Experience with Kafka and event-driven data architectures.
- Familiarity with machine learning, recommendation systems, reinforcement learning, or decision intelligence platforms.
- Experience with Azure Data Factory, Azure Data Lake Storage, Azure Event Hubs, or similar cloud-native data services.
- Experience with data observability and quality platforms such as Great Expectations, Monte Carlo, or equivalent tools.
- Experience integrating external healthcare, claims, CMS, CDC, consumer, or social determinants of health datasets.
- Background in healthcare, insurance, or another regulated industry with PHI/HIPAA handling requirements.
We build with modern AI development tools (such as Claude and GitHub Copilot) and expe