FanDuel
Data Engineer
New York City$116k–$145kmidAdded yesterday
About this role
Join FanDuel's data engineering team to design and maintain scalable data pipelines that power analytics and machine learning across the company. You'll collaborate with cross-functional teams to build reliable data infrastructure while ensuring quality and supporting critical business operations.
What you'll do
- Design and build scalable batch and streaming data pipelines using Python, SQL, and Spark
- Monitor, troubleshoot, and optimize data pipelines to ensure reliability and performance
- Implement data quality checks and observability practices
- Collaborate with analysts, scientists, and product managers to translate business needs into engineering solutions
- Document data architecture, transformations, and design decisions
- Contribute to team-wide best practices and participate in code reviews
What they're looking for
- SQL and Python (or Java/Scala)
- Data pipeline tools (Airflow, Databricks, Spark, Kafka, dbt)
- Cloud platforms (AWS, GCP, or Azure)
- Data modeling and ETL/ELT patterns
- Data warehousing concepts
- Version control and CI/CD practices
- Data governance and quality assurance
- Agile development workflows
Benefits
- Health plans with wellness, fertility, 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
- Flexible health plan options starting at $0 per paycheck
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 data pipeline you built—what were the key challenges and how did you ensure data quality?
- How do you approach translating ambiguous business requirements from stakeholders into concrete engineering tasks?