The Football Information Systems team at the Tennessee Titans builds the software and data foundation powering our football organization, including scouting, coaching, analytics, medical, and sports performance. We are seeking a Data Engineer to own the data infrastructure behind our football solutions. You will turn raw third-party data into a clean, reliable data platform that our staff can trust. Working with a modern data stack, you will design and maintain data pipelines and models that support analytics and applications across the organization.

Responsibilities

Qualifications:

Design, build, and optimize scalable ETL pipelines and data models for a data warehouse, ensuring performance, cost-efficiency, and adaptability.
Own the import of third-party football data, including cleaning, transforming, reconciling inconsistencies, and merging into the existing data model.
Manage Docker-based deployments of data services to ensure repeatable and reliable delivery across development and production environments.
Oversee end-to-end delivery of data engineering projects to internal stakeholders.
Explore using AI to help design data models, manage data pipelines, and address data anomalies.
3+ years of hands-on data engineering experience.
Expertise in SQL and Python, with experience building production-grade ETL pipelines.
Experience with Snowflake and DBT (or a comparable cloud data warehouse and transformation framework).
Deep understanding of data warehousing concepts and best pract

Responsibilities

Qualifications:

Design, build, and optimize scalable ETL pipelines and data models for a data warehouse, ensuring performance, cost-efficiency, and adaptability.
Own the import of third-party football data, including cleaning, transforming, reconciling inconsistencies, and merging into the existing data model.
Manage Docker-based deployments of data services to ensure repeatable and reliable delivery across development and production environments.
Oversee end-to-end delivery of data engineering projects to internal stakeholders.
Explore using AI to help design data models, manage data pipelines, and address data anomalies.
3+ years of hands-on data engineering experience.
Expertise in SQL and Python, with experience building production-grade ETL pipelines.
Experience with Snowflake and DBT (or a comparable cloud data warehouse and transformation framework).
Deep understanding of data warehousing concepts and best pract

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