FanDuel
Data Engineer
Atlanta, Georgia, United States$116k–$145kmidAdded yesterday
About this role
FanDuel is seeking a Data Engineer to design, build, and maintain scalable data pipelines that support analytics, machine learning, and business operations. You'll collaborate across teams to translate business needs into technical solutions while ensuring data reliability and platform quality.
What you'll do
- Design and build scalable batch and streaming data pipelines using Python, SQL, and Spark
- Monitor data pipelines, troubleshoot issues, and implement data quality checks
- Collaborate with analysts, scientists, and product managers to clarify requirements and deliver solutions
- Document data sources, transformations, and architecture decisions for maintainability
- Participate in code reviews, sprint planning, and contribute to team best practices
- Lead projects to build or improve data products with well-defined scope
What they're looking for
- SQL and Python programming
- Apache Spark and data pipeline tools (Airflow, dbt, Kafka)
- Cloud platforms (AWS, GCP, or Azure)
- Data modeling and ETL/ELT best practices
- Data warehousing concepts
- Version control and CI/CD workflows
- Data governance and compliance practices
- Troubleshooting and observability
Benefits
- Health plans starting at $0 per paycheck 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% company match
- Commuter benefits and pet insurance
- Fitness benefits
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've built—what were the key design decisions and how did you ensure reliability?
- How do you approach identifying and fixing data quality issues in production pipelines?