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# Data Engineer

- **Company:** FanDuel
- **Location:** Edinburgh, UK
- **Experience:** Experienced
- **Salary:** £56,572.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Amazon Web Services, Data Analysis, Microsoft Azure, Big Data, Software as a Service, Software Quality, Code Review, Data Architecture, Information Engineering, Data Governance, Data Infrastructure, Extract Transform Load (ETL), Data Systems, Distributed Systems, Interoperability, Python (Programming Language), Machine Learning, Scala (Programming Language), Data Streaming, Data Processing, Scripting, Real Time Systems, Apache Spark, Reliability of Systems, Event Driven Architecture, Containerization, Apache Kafka, Build Process, Data Pipelines, Serverless Computing, Databricks
- **Published:** October 9, 2026
- **Apply:** https://www.adzuna.co.uk/jobs/details/5895227402

## About the Role

* 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

## Description

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