Job description

WORK OPTION: The NBA currently provides eligible employees the option of working remotely one day per week. _________________ Position Summary The NBA’s Basketball Strategy & Growth department is seeking an analytics engineer to own the ingestion and transformation of the data that powers the Integrity Team’s work on the league’s gaming policy. The role is responsible for turning disparate and inconsistent external feeds — data from betting partners and operators, open-source intelligence (OSINT), and third-party vendors — into well-modeled, tested, and documented datasets, and for integrating those feeds with internal league basketball data so that betting activity can be evaluated in the context of what happened on the court. These models are the foundation for downstream data science and machine learning work: the anomaly detection, alerting, and AI-driven software the Integrity Team uses to surface conduct that may violate the gaming policy. The analytics engineer joins a team of data scientists, engineers and basketball experts and collaborates closely with the Legal department on any investigation relating to a potential violation of the gaming policy. The work is confidential, frequently time-sensitive, and expected to hold up to legal scrutiny — lineage, definitions, and data quality controls matter as much as speed. Major Responsibilities Own the ingestion of data from disparate betting partners, operators, and data vendors — each with its own schema, granularity, delivery cadence, and idiosyncrasies — and normalize them 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 the entity resolution work required to link external identities to known accounts and subjects. Integrate betting and OSINT data with internal league basketball data — schedule, play-by-play, box score, tracking, officiating, and player availability data — so that activity can be analyzed in game context. Develop production-ready analytical data models in dbt on Snowflake, applying dimensional modeling and analytics engineering best practices, including layered staging and mart designs, incremental models, and consistent naming conventions. Create curated, reusable pipelines and feature-ready datasets that support downstream data science and machine learning work, including model development, backtesting, and production inference. Implement automated data quality testing, freshness and volume monitoring, and reconciliation checks on incoming feeds so that vendor changes, outages, or silent data loss are detected before they affect investigative output. Build and manage orchestration and scheduling for pipelines, including dependency management, retries, alerting, and service levels appropriate to time-sensitive feeds. Develop and maintain a semantic layer with standardized business definitions and metrics so that analysts, data scientists, and the Legal department work from a single version of the truth. Document models, sources, lineage, and assumptions to support knowledge transfer, auditability, and the evidentiary needs of investigations. Onboard new betting partners and data providers — evaluating feed quality, defining requirements and specifications with providers, 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 the full stack engineer to expose those datasets to the platform’s backend jobs and UI. Apply appropriate access controls, data classification, and retention practices to sensitive betting, personal, and investigative data. Required Skills/Knowledge Advanced SQL skills with demonstrated experience developing 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 such as 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, and market maker behavior — and of basketball data such as play-by-play, box score, and tracking data. 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, and 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 to support trusted analytical data assets. Experience building pipelines that serve downstream data science and machine learning consumers. Familiarity with Git-based development workflows (GitHub or Azure DevOps), code review, and CI practices. Strong communication and stakeholder management skills, with the ability to translate business and investigative requirements into scalable analytical solutions for both technical and non-technical audiences, including attorneys and investigators. Sound judgment and discretion in handling confidential and legally sensitive information. Confident individual, able to work in a fast-paced environment and manage short-term deliverables along with long-term projects. Excels when working as part of a team, collaborating effectively both within BSG Integrity and cross-functionally. Preferred Skills/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 such as feature pipelines, model registries, training/retraining workflows, and model monitoring. Prior work on integrity, surveillance, fraud, AML, or trust and safety data is a plus. Experience in sports, gaming, or another regulated or compliance-driven environment is a plus. Experience/Education 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. Salary Range: $130,000-$150,000 Job Posting Title: Senior Manager Employees currently are eligible to receive an annual discretionary performance bonus, awarded at the sole discretion of the Company and subject to any terms and conditions set by the Company. Employees and/or eligible dependents may be eligible to participate in the following Company-sponsored employee benefit programs: medical; dental; vision; 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; a 401(k) retirement plan; and time off benefits that include vacation, sick time, and personal days. We Consider Applicants For All Positions On The Basis Of Merit, Qualifications And Business Needs, And Without Regard To Race, Color, National Origin, Religion, Sex, Gender Identity, Age, Disability, Alienage Or Citizenship Status, Ancestry, Marital Status, Creed, Genetic Predisposition Or Carrier Status, Sexual Orientation, Veteran Status, Familial Status, Status As A Victim Of Domestic Violence Or Any Other Status Or Characteristic Protected By Applicable Federal, State, Or Local Law. The NBA is committed to providing a safe and healthy workplace. To safeguard our employees and their families, our visitors, and the broader community from COVID-19, and in consideration of recommendations from health authorities and the NBA’s own advisors, any individual working onsite in our New York and New Jersey offices must be fully vaccinated against COVID-19. The NBA will discuss accommodations for individuals who cannot be vaccinated due to a medical reason or sincerely held religious belief, practice, or observance. About the NBA The National Basketball Association (NBA) is a global sports and media organization with the mission to inspire and connect people everywhere through the power of basketball. Built around five professional sports leagues: the NBA, WNBA, NBA G League, NBA 2K League and Basketball Africa League, the NBA has established a major international presence with games and programming available in 214 countries and territories in 60 languages, and merchandise for sale in more than 200 countries and territories on all seven continents. NBA rosters at the start of the 2024-25 season featured a record-tying 125 international players from a record-tying 43 countries. NBA Digital’s assets include NBA TV, NBA.com, the NBA App and NBA League Pass. The NBA has created one of the largest social media communities in the world, with more than 2.3 billion likes and followers globally across all leagues, team and player platforms. NBA Cares, the NBA’s global social responsibility platform, partners with renowned community-based organizations around the world to address important social issues in the areas of education, inclusion, youth and family development, and health and wellness.

Requirements

Must-haves

WORK OPTION: The NBA currently provides eligible employees the option of working remotely one day per week. _________________
Major Responsibilities
Build and maintain ingestion pipelines for OSINT and other unstructured or semi-structured sources, including the entity resolution work required to link external identities to known accounts and subjects.
Integrate betting and OSINT data with internal league basketball data — schedule, play-by-play, box score, tracking, officiating, and player availability data — so that activity can be analyzed in game context.
Create curated, reusable pipelines and feature-ready datasets that support downstream data science and machine learning work, including model development, backtesting, and production inference.
Build and manage orchestration and scheduling for pipelines, including dependency management, retries, alerting, and service levels appropriate to time-sensitive feeds.
Develop and maintain a semantic layer with standardized business definitions and metrics so that analysts, data scientists, and the Legal department work from a single version of the truth.
Document models, sources, lineage, and assumptions to support knowledge transfer, auditability, and the evidentiary needs of investigations.
Onboard new betting partners and data providers — evaluating feed quality, defining requirements and specifications with providers, 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 the full stack engineer to expose those datasets to the platform’s backend jobs and UI.
Apply appropriate access controls, data classification, and retention practices to sensitive betting, personal, and investigative data.
Required Skills/Knowledge
Advanced SQL skills with demonstrated experience developing 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 such as 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, and market maker behavior — and of basketball data such as play-by-play, box score, and tracking data.
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, and unstructured sources.
Experience building and operating orchestrated data pipelines, including scheduling, dependency management, retries, and monitoring.

Nice-to-haves

Preferred Skills/Knowledge
Prior work on integrity, surveillance, fraud, AML, or trust and safety data is a plus.
Experience in sports, gaming, or another regulated or compliance-driven environment is a plus.
Bachelor’s degree required in a technical or quantitative field; master’s degree preferred.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningaisqlpythonapiazureml

Platform: workday
Effort: long

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Wellness