Data Engineering Manager (LTV and MarTech)

Trainline
City of London
3 months ago
Applications closed

Related Jobs

View all jobs

Data Engineering Manager

Data Engineering Manager

BDO Digital Data Architecture and Engineering Senior Manager/Director

Data Science Manager – Property Tech – London

BI and Data Engineering Lead

BI and Data Engineering Lead

About Trainline

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.

Role Purpose

Lead an embedded, cross‑functional team of Data, MarTech, and Machine Learning Engineers to build and activate Trainline’s customer data foundations — powering personalised marketing, customer insight, and performance measurement across our marketing channels.

As a Data Engineering Manager at Trainline you will…

Lead and coach an agile team of Data Engineers, Martech, and Machine Learning Engineers deeply embedded in the business, building reliable pipelines, high‑quality data assets and reusable audiences using dbt, Spark, SQL, Python, Airflow on AWS and activating data for personalization, marketing and advertising.

  • Be a 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.
  • Partner with Legal and Privacy teams to define and embed consent management and data governance best practices across tagging, activation, and attribution.
  • Design and scale integrations between marketing platforms, analytics systems and CRM to enable reliable measurement, attribution and personalization.
  • Seek opportunities to embed the latest in LLM and other AI technologies in our data products for efficiency, repeatability and reliability.
We’d love to hear from you if you…
  • 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 background in DevOps, 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, DynamoDB, MSK).
  • Have a grounding in web/app tracking for analytics and marketing.
  • Familiarity with analytics and activation platforms such as GA4, Census, HighTouch, Segment, Tealium, Braze, Salesforce Marketing Cloud, or equivalent tools.
  • Understand data privacy and consent frameworks (GDPR, CCPA) and their technical implementation in tagging and activation systems.
More Information

Enjoy fantastic perks like private healthcare & dental insurance, a generous work‑from‑abroad policy, 2‑for‑1 share purchase plans, an EV scheme, extra festive time off, and excellent family‑friendly benefits. We prioritise career growth with clear career paths, transparent pay bands, personal learning budgets, and regular learning days. Jump on board and supercharge your career from day one!

Values
  • Think Big – We're building the future of rail.
  • Own It – We focus on every customer, partner and journey.
  • Travel Together – We're one team.
  • Do Good – We make a positive impact.
Diversity & Inclusion

We know that having a diverse team makes us better and helps us succeed. And we mean all forms of diversity – gender, ethnicity, sexuality, disability, nationality and diversity of thought. We are committed to creating inclusive places to work, where everyone belongs and differences are valued and celebrated.


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Careers

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

Jobs

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

Education

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.