Analytics Engineer, Integrity

DataJobs; New York (NY); Hybrid; USD 130,000 - 150,000; Full time

Benefits offered by this job

Annual bonus

Medical, dental, vision

401(k)

Wellbeing & education support

Job summary

The NBA’s Analytics Engineer role in the Integrity Team focuses on building production-ready datasets by integrating external betting feeds with internal basketball data. You’ll enable anomaly detection, AI-driven capabilities, and ensure data governance under legal scrutiny.

You will own ingestion, OSINT pipelines, and semantic layer maintenance, collaborating with data scientists and full-stack engineers to expose datasets for downstream analytics.

Qualifications

• Bachelor’s degree in a technical or quantitative field.
• Minimum 5 years of experience in analytics engineering or data engineering.
• Experience building analytical data models on cloud platforms.

Responsibilities

• Own ingestion from betting partners and data vendors, normalizing schemas into a conformed model.
• Build and maintain OSINT ingestion pipelines with entity resolution.
• Integrate betting/OSINT data with internal datasets for game context.
• Develop production analytical data models using dbt on Snowflake.
• Create feature-ready datasets for data science and ML workflows.
• Implement automated data quality tests, freshness, and monitoring.

Skills

Advanced SQL

dbt

Snowflake

Dimensional modeling

Sports betting knowledge

Python

Vendor integration

Orchestration & scheduling

Semantic layer

Data quality & monitoring

ML data pipelines

Git workflows

Stakeholder communication

Discretion & handling sensitive data

Independent work

Cross-functional collaboration

Education

Bachelor’s degree in technical/quantitative field

Master’s degree preferred

Tools

dbt

Snowflake

Python

GitHub

Azure DevOps

Databricks

Airflow

Dagster

Prefect

MLflow

Job description

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.

What you’ll do

• 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.

What you bring

• 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 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 for handling confidential, legally sensitive information.
• Ability to work independently in a fast-paced environment while balancing short-term deliverables with long-term initiatives.
• Strong teamwork and cross-functional collaboration within BSG Integrity and beyond.

Technology stack

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

Preferred skills

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

Education and experience

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

Location and work option

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

Compensation

• USD 130,000 - 150,000 per year

Benefits

• Annual discretionary performance bonus, at the Company’s sole discretion and subject to Company terms and conditions
• Medical, dental, and vision
• Life/AD&D insurance
• Short- and long-term disability
• Fertility and family-forming assistance
• Wellbeing allowanceEducational 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