FanDuel
Analytics Engineer
About this role
Join FanDuel's Sports Modeling & Innovation team as an Analytics Engineer to design and maintain data models, pipelines, and visualizations that power data-driven business decisions. You'll bridge data engineering and data science, optimizing data ingestion, transformation, and analysis across SQL, Databricks, and cloud platforms.
What you'll do
- Develop and maintain fast data ingestion pipelines into databases (SQL, Delta Lakes) with real-time or near-real-time capabilities
- Build and maintain validation pipelines to detect bad or missing data across growing database ecosystems
- Manage data transformation jobs, optimize code performance, and troubleshoot issues for the Sports Modeling team
- Automate data cleaning and database management on large datasets
- Optimize query execution times and remove unnecessary metrics by balancing memory and speed tradeoffs
- Create and own Tableau dashboards and R Shiny app additions
What they're looking for
- SQL and relational database design
- Python and R for data pipeline development
- Databricks platform experience
- AWS S3 and cloud storage
- Tableau for data visualization
- Command line and scripting
- Data ingestion and ETL pipeline design
- Problem-solving and optimization
Benefits
- Health plans with fertility, family planning, and mental health support
- Generous paid time off and sick leave
- Annual bonus and long-term incentive opportunities
- 401k with up to 5% match
- Commuter benefits and pet insurance
- Flexible work environment with collaborative team culture
Opens the official application on the employer’s site. No login required.
FanDuel
FanDuel operates a sports betting and gaming platform, handling payments, wallet systems, and player experiences at scale. The company is hiring Security Operations Engineers for threat detection and incident response, as well as Security Engineers to embed security practices across its development lifecycle and address emerging AI/LLM risks.
- Website
- fanduel.com
Likely interview questions
- Walk us through a complex data pipeline you built—what tools did you use and how did you handle data validation?
- Describe your experience optimizing query performance and reducing execution times in large datasets.