Data Engineer

ABOUT FANDUEL

Fan Duel Group is the premier mobile gaming company in the United States and Canada. Fan Duel Group consists of a portfolio of leading brands across mobile wagering including:
America’s #1 Sports book, Fan Duel Sports book; its leading iGaming platform, Fan Duel Casino; the industry’s unquestioned leader in horse racing and advance-deposit wagering, Fan Duel Racing; and its daily fantasy sports product.

In addition, Fan Duel Group operates Fan Duel TV, its broadly distributed linear cable television network and Fan Duel TV+, its leading direct-to-consumer OTT platform. Fan Duel Group has a presence across all 50 states, Canada, and Puerto Rico.

The company is based in New York with US offices in Los Angeles, Atlanta, and Jersey City, as well as global offices in Canada and Scotland. The company’s affiliates have offices worldwide, including in Ireland, Portugal, Romania, and Australia.

Fan Duel Group is a subsidiary of Flutter Entertainment, the world's largest sports betting and gaming operator with a portfolio of globally recognized brands and traded on the New York Stock Exchange (NYSE: FLUT).

THE POSITION
Our roster has an opening with your name on it

We are seeking a Staff Data Engineer to lead the technical design and implementation of our most critical data infrastructure and products. In this senior-level individual contributor role, you’ll be responsible for designing scalable systems, setting data architecture standards, and solving complex technical challenges that power analytics, data science, and business function use cases across the company.

You will work closely with engineers, product managers, and business stakeholders to architect data solutions that are performant, reliable, and built with a long-term, customer-centric mindset. If you’re excited by this challenge and want to work within a dynamic company, then we’d love to hear from you.

In addition to the specific responsibilities outlined above, employees may be required to perform other such duties as assigned by the Company. This ensures operational flexibility and allows the Company to meet evolving business needs.

THE GAME PLAN
Everyone on our team has a part to play

Architect High-Impact Data Systems

• Design and implement scalable, maintainable, and secure batch & streaming data pipelines and architectures that support enterprise-wide data needs
• Define standards for data modeling, data product design, and pipeline orchestration using modern tools and cloud-native technologies
• Collaborate with cross-functional stakeholders to translate business and analytical requirements into end-to-end data solutions
• Drive Engineering Best Practices
• Establish and enforce engineering best practices around code quality, testing, documentation, and deployment
• Contribute to the evolution of the data platform, ensuring systems are modular, interoperable, and resilient
• Lead technical design and code reviews, mentoring peers and raising the bar for engineering excellence

Lead Strategic Initiatives

• Partner with data platform teams, analytics, and data science to deliver reusable data assets and shared infrastructure
• Identify and resolve architectural bottlenecks in the current data platform and propose improvements that reduce complexity and boost performance
• Drive initiatives that improve data quality, lineage, observability, and system reliability

Influence and Collaborate Across Teams

• Act as a technical liaison between engineering, product, and analytics teams, ensuring alignment on architecture and data strategy
• Provide technical leadership and guidance to other data engineers and contribute to the team’s overall growth and maturity
• Help evaluate and onboard new technologies, frameworks, and practices to keep our stack modern and efficient

THE STATS
What we're looking for in our next teammate

• 8+ years of experience in data engineering or a related field, with a focus on building scalable data systems and platforms.
• Strong expertise in modern data tools and frameworks such as Spark, dbt, Airflow, Kafka, Databricks, and cloud-native services (AWS, GCP, or Azure)
• Deep understanding of data modeling, distributed systems, ETL/ELT pipelines, and streaming…