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Analytics Engineer, Integrity

NBA New York, NY

New York, NY

Today

$130k - $150k/yr Full time Analytics Engineer Analytics Engineering Azure DevOps

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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 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

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