Integrity Analytics Engineer: Data Pipelines & Modeling

National Basketball Association (NBA); New York (NY); Hybrid; USD 130,000 - 150,000; Full time

Benefits offered by this job

Annual discretionary bonus

Medical, dental, vision benefits

Job summary

The National Basketball Association (NBA) is seeking an analytics engineer in the Basketball Strategy & Growth department to own ingestion and transformation of betting, OSINT, and vendor data. You will integrate external feeds with internal league data and build models that support analytics, ML, and investigations into gaming policy.

You will collaborate with data scientists, engineers, and legal to ensure data lineage, quality, and timely delivery in a fast-paced environment.

Qualifications

• Bachelor’s degree in a technical or quantitative field.
• Minimum 5 years of analytics engineering or data modeling experience.
• Experience with cloud-based data platforms and data pipelines.

Responsibilities

• Own ingestion of data from betting partners and vendors and normalize into a conformed model.
• Build and maintain OSINT and structured data ingestion pipelines.
• Integrate external data with internal league basketball data for context.
• Develop production-ready analytical data models in dbt on Snowflake.
• Create reusable pipelines and datasets for data science workflows.
• Implement automated data quality testing and monitoring.

Skills

Advanced SQL

dbt

Snowflake

Python

Data integration

Airflow

Entity resolution

Git workflows

Education

Bachelor’s degree in a technical/quantitative field

Master’s degree preferred

Tools

dbt

Snowflake

GitHub/Azure DevOps

Databricks

Airflow

Job description

The National Basketball Association (NBA) is seeking an analytics engineer in the Basketball Strategy & Growth department to own ingestion and transformation of betting, OSINT, and vendor data. You will integrate external feeds with internal league data and build models that support analytics, ML, and investigations into gaming policy.

You will collaborate with data scientists, engineers, and legal to ensure data lineage, quality, and timely delivery in a fast-paced environment.