Design, build, maintain, and enhance data models, datasets, and visualizations used by data scientists to drive business performance.
Bridge the data engineering team with data scientists, analysts, and stakeholders to leverage data for insights and decisions.
Develop pipelines for rapid, sometimes real-time, data ingestion into well-structured databases (SQL, Delta Lakes, etc.).
Maintain validation pipelines to flag bad or missing data and iterate as new databases are created.
Manage existing data transformation jobs for the Sports Modeling and Innovation team, optimize code across code bases, and troubleshoot issues.
Automate data cleaning projects on large datasets as part of ongoing database management.
Balance memory and speed tradeoffs to optimize project performance, aiming to reduce query times and prune unused metrics.
Create and own a backlog of Tableau dashboard and R Shiny app enhancements.
Curiosity about AI and emerging technologies is a plus.

3+ years of experience in an analytics or data engineering role.
2+ years maintaining databases with a focus on improving data ingestion pipelines.
Deep understanding of relational databases.
Proficient in SQL, Databricks, R/Python, Tableau, AWS S3, and command line usage.
Experience building data pipelines using Python or R and Databricks is a strong advantage.
Bachelor's Degree, preferably in a numerate field (Math, Stats, Econ, etc.) or relevant data analysis experience.
Strong organizational and time-management skills.
Ability to work effectively in a fast-paced environment.
Solid problem-solving abilities and collaborative mindset.
Experience working on teams on collaborative projects.

Analytics Engineer

Job Description