Analytics Engineer, Integrity

The NBA’s Basketball Strategy & Growth department is hiring an Analytics Engineer for the Integrity Team, working on data that supports the league’s gaming policy. This role focuses on building production-ready datasets by bringing together external betting and OSINT feeds with internal basketball data, enabling anomaly detection, alerting, and AI-driven capabilities under legal scrutiny.

• Own ingestion from betting partners, operators, and data vendors, then normalize their varying schemas, granularity, delivery cadence, and quirks into a consistent, conformed model of markets, wagers, prices, and accounts
  .
• Build and maintain ingestion pipelines for OSINT and other unstructured or semi-structured sources, including entity resolution to link external identities to known accounts and subjects.
• Integrate betting and OSINT data with internal league basketball datasets including schedule, play-by-play, box score, tracking, officiating
  , and player availability so activity can be analyzed in the context of games.
• Develop production analytical data models in dbt on Snowflake using dimensional modeling and analytics engineering best practices such as layered staging and mart designs, incremental models, and consistent naming conventions.
• Create curated, reusable pipelines and feature-ready datasets for downstream data science and machine learning, including model development, backtesting
  , and production inference
  .
• Implement automated data quality testing along with freshness and volume monitoring and reconciliation checks so vendor changes, outages, or silent data loss are detected before investigative outputs are impacted.
• Design orchestration and scheduling for pipelines, including dependency management, retries, alerting, and service levels for time-sensitive feeds.
• Maintain a semantic layer with standardized business definitions and metrics so analysts, data scientists, and the Legal department can rely on a single version of the truth.
• Document models, sources, lineage, and assumptions to support knowledge transfer, auditability, and evidentiary needs.
• Onboard new betting partners and data providers by evaluating feed quality, defining requirements and specifications, and absorbing schema changes without breaking downstream consumers.
• Partner with data scientists to translate analytical and modeling requirements into scalable data models, and with full stack engineering to expose datasets for backend jobs and the UI.
• Apply appropriate access controls
  , data classification, and retention practices for sensitive betting, personal, and investigative data.

• Advanced SQL with experience building complex analytical data models.
• Production experience with dbt
  , including testing, documentation, macros, and managing a large model DAG.
• Production experience with Snowflake
  , including performance and cost management (warehouse sizing, clustering, and query tuning).
• Strong understanding of dimensional modeling, data warehousing, and analytics engineering best practices.
• Understanding of sports betting markets (odds, line movement, limits, player props, market maker behavior) and familiarity with basketball data such as play-by-play, box score, and tracking.
• Python proficiency for ingestion, API integration, and data processing.
• Experience integrating multiple external partners or vendors, including messy, high-volume, semi-structured, and unstructured sources.
• Experience operating orchestrated data pipelines with scheduling, dependency management, retries, and monitoring.
• Experience building semantic layers, standardized definitions, and reusable analytical datasets.
• Experience implementing automated data quality testing, monitoring, and documentation to support trusted analytics.
• Experience…