Analytics Engineer, Integrity

Sports Careers; New York (NY); Hybrid; USD 120,000 - 160,000; Full time

Job summary

NBA in New York seeks an Analytics Engineer to own ingestion and transformation of data powering the Integrity Team’s gaming policy initiatives. You will consolidate feeds from betting partners, OSINT, and vendors into a unified model and integrate with internal basketball data for in-game context.

You will develop production analytics models in dbt on Snowflake, ensure data quality, and collaborate with data scientists and lawyers on investigations.

Qualifications

• 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 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.

Responsibilities

• Own the ingestion of data from disparate betting partners, operators, and data vendors and normalize them into a 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.
• Integrate betting and OSINT data with internal league basketball data to analyze activity in game context.
• Develop production-ready analytical data models in dbt on Snowflake with dimensional modeling, layered staging and mart designs.
• Create reusable pipelines and feature-ready datasets to support data science and machine learning work.
• Implement automated data quality testing, freshness and volume monitoring, and reconciliation checks on incoming feeds.
• Build and manage orchestration and scheduling for pipelines with dependency management and alerting.
• Develop a semantic layer with standardized definitions and metrics for consistency across teams.
• Document models, sources, lineage, and assumptions for auditability.
• Onboard new betting partners and adapt to vendor schema changes without breaking downstream consumers.
• Collaborate with data scientists and full-stack engineers to expose datasets to platforms and UI.
• Apply access controls and retention practices to sensitive betting and investigative data.

Skills

Advanced SQL

dbt

Snowflake

Dimensional modeling

Data warehousing

Python

API integration

Data pipelines

Collaboration

Vendor integration

Tools

dbt

Snowflake

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 pipelin