Title: Senior Data Engineer, Basketball Data Strategy

Department: Basketball Data Strategy, Basketball Operations

Reports to: Director, Basketball Data Strategy

Position Summary: The Senior Data Engineer, Basketball Data Strategy is a critical member of the Basketball Data Strategy team. Supporting the Director, Basketball Data Strategy, they will architect, build, and maintain a modern cloud data platform spanning ingestion, transformation, storage, and service layers, and utilizing statistical feeds from external vendors and internal sources. This position is responsible for the end-to-end design, implementation, and maintenance of data infrastructure, and includes creation of new data infrastructure in a greenfield stack, owning architectural decisions across the full data lifecycle. This position works alongside the Data Science / Analyst teams to ensure the cleanliness, integrity, accuracy, and relevance of the data to be analyzed, and alongside the Software Development team to deliver data in performant web and mobile products. This position will optimize existing data structures, lead platform migrations, and regularly audit the Basketball Data Strategy data ecosystem, to ensure curated, production-grade datasets are performant, validated, and optimized for predictive modeling and visualization.

Essential Functions (Duties & Responsibilities**):

• Build data foundation: architect and oversee the basketball data ecosystem, including sources / streams such as NBA data, non-NBA basketball data, player tracking data, natural language / qualitative data (e.g. scouting reports), and health/performance/wearables data.
• Create data pipelines: design, build, and maintain automated systems using cloud-native orchestration tools (e.g. Airflow) that collect, clean, and organize data from internal and third-party sources; enforce schema and data lineage tracking across all pipeline stages
• Lead platform optimization & migration efforts, including refactoring legacy pipelines and translating to medallion architectures while integrating transformation logic and lineage (e.g. dbt); optimize storage formats (e.g. Parquet); implement partitioning and indexing strategies; improve existing data systems with focus on speed and reliability
• Evaluate, propose, and execute platform decisions on emerging technologies / providers / strategies for storage and compute
• Optimize 3D tracking motion capture data ingestion and processing for use by analysts
• Ensure data quality: implement rigorous data validation frameworks and observability tooling to enforce schema, referential integrity, end-to-end data lineage, and data hierarchies among multiple data sources, including automated quality checks, unit testing of transformation logic, and proactive alerts
• Be knowledgeable and up-to-date in the global basketball data landscape

**Responsibilities subject to change based on organizational needs and direction from management.

Education:

• Bachelor’s in Statistics, Computer Science, Engineering, or a related field, or equivalent academic or professional experience; experience working with database solutions in a professional best-practices environment

Minimum Qualifications: 

• 6+ years of experience in a similar role
• Database expertise: expert-level proficiency in relational database design (e.g. SQL Server, PostgreSQL, Snowflake) with deep knowledge of best practices (e.g. normalization, index strategies, query plan optimization). Experience with nonrelational data structures – document stores, time-series solutions, vector storage.
• Hands-on experience architecting data platforms with a track record of greenfield builds or major platform migrations; ; cloud platform experience on AWS (preferred) or similar platforms
• Pipeline tools: experience with workflow tools like Airflow
• Container technology: experience with Docker and Kubernetes
• Understanding of modern ML techniques and end-to-end MLOps workflows on cloud platforms in production environments, including model versioning, deployment pipelines, and monitoring. Fluency in DevOps (CI/CD, infrastructure-as-code) treating data and ML infrastructure as production software
• Strong Python and SQL skills; experience with data transformation tools
• Big data: experience with tools that handle large datasets; ability to work with high-precision location/movement data (sub-second sampling)

Other Qualifications:

• A strong sense of organization
• High agency
• Adaptability and “outside-the-box” thinking
• Knowledge of and passion for NBA basketball

Location: El Segundo (office M‐F), and other occasional off‐site events

Travel: Less than 5% of the time 

Hours: Full-time. Must be available to work evenings, weekends and holidays as reasonably required

The pay range for this role is $185,000 - $210,000 annually. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to skill set, depth of experience and certifications. In addition to those factors, we consider the relative pay of our current employees in similar positions when making a final offer.

We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, sexual orientation, age, disability, gender identity, marital or veteran status, or any other protected class.