Analytics Engineer, Integrity

The NBA’s Basketball Strategy & Growth department is hiring an Analytics Engineer to ingest, transform, and model data that supports the Integrity Team’s work related to the league’s gaming policy. This role focuses on building production-ready datasets by combining external betting and OSINT feeds with internal basketball data to enable anomaly detection, alerting, and AI-driven workflows under legal scrutiny.

Location and Work Option

New York, NY (hybrid). The NBA provides eligible employees the option of working remotely one day per week.

Role Summary

The position owns end-to-end analytics engineering for data integration and modeling, including normalization across betting partners, OSINT ingestion, and integration with internal basketball datasets. Deliverables include curated feature-ready datasets, automated data quality monitoring, and a semantic layer that supports consistent reporting for analysts, data scientists, and the Legal department.

Responsibilities

• Own ingestion of data from disparate betting partners, operators, and data vendors, normalizing markets, wagers, prices, and accounts into a consistent conformed model.
• Build and maintain ingestion pipelines for OSINT and other unstructured or semi-structured sources, including entity resolution to connect external identities to known accounts and subjects.
• Integrate betting and OSINT data with internal league basketball data, including schedule, play-by-play, box score, tracking, officiating, and player availability, enabling analysis in game context.
• Develop production-ready analytical data models in dbt on Snowflake, using dimensional modeling and analytics engineering best practices (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, supporting model development, backtesting, and production inference.
• Implement automated data quality testing, freshness and volume monitoring, and reconciliation checks to detect vendor changes, outages, or silent data loss before investigative output is impacted.
• Build and manage orchestration and scheduling for pipelines, including dependency management, retries, alerting, and service levels for time-sensitive feeds.
• Develop and maintain a semantic layer with standardized business definitions and metrics to provide 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 vendor 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 to backend jobs and the UI.
• Apply access controls, data classification, and retention practices for sensitive betting, personal, and investigative data.

Required Qualifications

• Advanced SQL with experience building complex analytical data models.
• Production experience with analytics engineering frameworks such as dbt, including testing, documentation, macros, and managing a large model DAG.
• Production experience with Snowflake, including performance and cost management (warehouse sizing, clustering, 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 basketball data (play-by-play, box score, tracking).
• Proficiency in Python for ingestion, API integration, and data processing.
• Experience integrating data from multiple external partners or vendors, including messy high-volume semi-structured or unstructured sources.
• Experience building and operating orchestrated data pipelines, including scheduling, dependency management, retries, and monitoring.
• Experience developing semantic layers, standardized business definitions, and reusable analytical datasets.
• Experience implementing automated data quality testing, monitoring, and documentation for trusted analytical assets.
• Experience building pipelines that serve downstream data science and machine learning consumers.
• Familiarity with Git-based workflows (GitHub or Azure DevOps), code review, and CI practices.
• Strong communication and stakeholder management skills, including translating business and investigative requirements for both technical and non-technical audiences such as attorneys and investigators.
• Sound judgment and discretion when handling confidential and legally sensitive information.
• Ability to operate as a confident individual contributor in a fast-paced environment while managing short-term deliverables and long-term projects.
• Ability to collaborate effectively within BSG Integrity and cross-functionally.

Preferred Skills and Knowledge

• Experience with Databricks, including Spark, Delta Lake, Unity Catalog, and/or ML lifecycle tooling such as MLflow.
• Experience with Airflow, Dagster, Prefect, or a comparable orchestration platform.
• Experience with entity resolution, identity matching, or record linkage.
• Familiarity with MLOps practices including feature pipelines, model registries, training/retraining workflows, and model monitoring.
• Prior work on integrity, surveillance, fraud, AML, or trust and safety data.
• Experience in sports, gaming, or another regulated or compliance-driven environment.

Education and Experience

• Bachelor’s degree required in a technical or quantitative field (master’s degree preferred).
• Minimum 5 years of experience in analytics engineering, data engineering, or developing analytical data models on cloud-based data platforms.

Technologies

• SQL
• dbt
• Snowflake
• Python
• GitHub
• Azure DevOps

Compensation

USD 130,000 - 150,000 per year

Benefits

• Annual discretionary performance bonus (subject to company terms and conditions).
• Medical, dental, and vision insurance
• Life/AD&D insurance
• Short- and long-term disability
• Fertility and family-forming assistance
• Wellbeing allowance
• Educational assistance
• Mental health coaching/therapy
• Tax advantaged accounts such as HSA and healthcare/dependent care FSAs
• 401(k) retirement plan
• Vacation, sick time, and personal days