FanDuel
Data Engineer
New York City$116k–$145kmidAdded today
About this role
FanDuel seeks a Data Engineer to design and maintain scalable data pipelines supporting analytics, machine learning, and business operations. You'll collaborate with cross-functional teams to build reliable data infrastructure and ensure data quality at scale using modern tools and cloud platforms.
What you'll do
- Design, build, and maintain scalable batch and streaming data pipelines using Python, SQL, and Spark
- Implement data quality checks and monitoring to ensure reliable, accurate, and timely data delivery
- Collaborate with data analysts, scientists, and product managers to translate business requirements into engineering solutions
- Troubleshoot data pipeline issues systematically and maintain observability practices
- Document data sources, transformations, and architectural decisions for long-term maintainability
- Participate in code reviews, sprint planning, and agile team activities
What they're looking for
- SQL and Python programming
- Databricks, Airflow, dbt, Spark, or Kafka
- Cloud platforms (AWS, GCP, or Azure)
- Data modeling and ETL/ELT best practices
- Data warehousing concepts
- Version control and CI/CD workflows
- Data quality and testing practices
- Communication and cross-functional collaboration
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
- 401(k) 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 built—what challenges did you encounter and how did you solve them?
- How do you approach designing a pipeline to handle both batch and streaming data requirements?