Data Engineering Manager; Analytics Engineering and BI

About us

We are champions of rail, inspired to build a greener, more sustainable future of travel. Trainline enables millions of travellers to find and book the best value tickets across carriers, fares, and journey options through our highly rated mobile app, website, and B2B partner channels.

Great journeys start with Trainline 🚄

Now Europe’s number 1 downloaded rail app, with over 125 million monthly visits and £5.9 billion in annual ticket sales, we collaborate with 270+ rail and coach companies in over 40 countries. We want to create a world where travel is as simple, seamless, eco-friendly and affordable as it should be.

Today, we're a FTSE 250 company driven by our incredible team of over 1,000 Train liners from 50+ nationalities, based across London, Paris, Barcelona, Milan, Edinburgh and Madrid. With our focus on growth in the UK and Europe, now is the perfect time to join us on this high-speed journey.

We are looking for a Data Engineering Manager to lead an embedded, cross-functional team of Data, Mar Tech, and Business Intelligence (BI) Engineers to build and activate Trainline’s data marts—powering insight and performance, predictive product features, and measurement across all products and digital channels.

• A committed lead and coach to an agile team of polyglot Data Engineers, Martech and BI Engineers deeply embedded in the business, building reliable pipelines, high-quality data assets using dbt, Spark, SQL, Python, Airflow on AWS.
• Be a brilliant people manager that motivates and engages their team to develop their skills and increase their impact.
• Lead the technical direction of the cross-functional team, making good choices on technologies and approach to get the biggest impact for the least risk.
• Partner with product managers to build a compelling and high-impact roadmap for the team, making the right trade-offs and priority calls and keeping pace with business change.
• Foster an obsession with quality and engineering excellence through automated, repeatable processes using CI/CD, TDD, BDD.
• Own the operation of the products built by your team and continuously improve operation performance, ensuring that the incident management process is flawlessly executed and all opportunities for learning are captured.
• Drive incremental growth in engineering maturity, embedding standards, tools and practices that allow repeatable and efficient delivery of products to production.
• Oversee the tagging and event instrumentation strategy across web and app, ensuring privacy-compliant, reliable data capture for analytics, marketing and experimentation platforms (e.g. via GTM, server-side tagging, and Consent Mode).
• Partner with Legal and Privacy teams to define and embed consent management and data governance best practices across tagging, activation, and attribution.
• Seek opportunities to embed the latest in LLM and other AI technologies in our data products for efficiency, repeatability and reliability.
• Coach the team to continuously improve agile maturity, self-organisation and delivery predictability.

• Thrive in a diverse, open and collaborative environment.
• People management and technical leadership experience.
• Are passionate about agile software delivery with a track record of leading effective agile and lean software teams.
• A consistent background in software development in high volume environments.
• Have a strong background in Dev Ops, deploying, managing and maintaining services using Docker, Terraform and AWS CLI tools to achieve infrastructure-as-code and automated deployments.
• Have an excellent working knowledge of AWS services (EMR, ECS, IAM, EC2, S3, Dynamo
  
  DB, MSK).
• Have a strong grounding in JavaScript, HTML/CSS, and web/app tracking for analytics and marketing.
• Familiarity with analytics tracking using tools such as GA4 and Adobe Analytics.
• Understand data privacy and consent frameworks (e.g. GDPR, CCPA) and their technical implementation in tagging.

We work with a modern, cloud-native data stack designed for scale, flexibility and experimentation:

• SQL, Python and Scala
• Kafka, Spark, Akka…