FanDuel Group is a world-class team of brands and products that deliver sports betting, gaming and entertainment to millions of US sports fans every day. That’s no easy task and wouldn't be possible without a fantastic team who have helped us pioneer new products and innovative features that make us a leader in the industry.

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.

FanDuel 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 Data Engineer to help lead the technical design and implementation of our most critical data infrastructure and products. 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.

THE GAME PLAN

Everyone on our team has a part to play

Implement High-Impact Data Systems

Implement and help design scalable, maintainable, and secure real-time and streaming data infrastructure and architectures that support enterprise-wide data needs
Define standards for real-time system design and real-time tooling using modern tools and cloud-native technologies, and driving technical roadmaps for multi-phase and cross-team initiatives
Collaborate with cross-functional stakeholders to translate business and analytical requirements into end-to-end data solutions

Drive Engineering Best Practices

Embody 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
Contribute to technical design and code reviews, mentoring peers and raising the bar for engineering excellence

Contribute to Strategic Initiatives

Partner with data platform, data engineering, analytics, data science, and product engineering teams to deliver reusable data assets and shared infrastructure
Identify and resolve structural and performance bottlenecks in the current data platform and propose improvements that reduce complexity and boost performance
Help define and deliver on metrics and initiatives that improve system reliability, scalability, data quality, lineage, and observability

THE STATS

What we’re looking for in our next teammate

3+ years of experience in data engineering or a related field, with a focus on building scalable data systems and platforms.
Expertise in modern streaming data tools and frameworks such as Kafka, Spark, Databricks, and cloud-native services (AWS, GCP, or Azure).
Deep understanding of streaming architectures, distributed systems, ETL/ELT pipelines, infrastructure-as-code, and build processes.
Proficiency at least one object-oriented programming language (e.g., Python, Java, Scala) and scripting
Demonstrated experience working on complex technical systems end-to-end, from design through production
Excellent communication skills with the ability to explain technical concepts to both technical and non-technical audiences

Preferred Qualifications

Experience working on complex and large-scale data platforms that support analytics, machine learning, and operational workloads
Familiarity with data governance, privacy, and compliance frameworks
Background in customer-centric or product-driven environments (e.g., digital, eCommerce, SaaS)
Experience with data modelling and data processing tooling and best practices