At a Glance
- Tasks: Design and build scalable data pipelines for machine learning workloads.
- Company: Join Trainline, Europe's leading independent rail platform focused on greener travel.
- Benefits: Enjoy private healthcare, work from abroad policy, and excellent family-friendly benefits.
- Other info: Collaborate with a diverse team and access clear career paths and personal learning budgets.
- Why this job: Make a positive impact on travel while supercharging your career in data engineering.
- Qualifications: Knowledge of Python, SQL, and experience with data pipelines in cloud environments.
The predicted salary is between 63000 - 77000 £ per year.
About us
At Trainline, our purpose is to empower greener travel choices, connecting people and places.
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
We're Europe's leading independent rail platform, helping millions of travellers find and book the best-value rail and coach journeys across our app, website and partner channels.
Our job is to make the green travel choice the best choice.
By building a better train travel experience, we help more people choose rail - creating a positive impact for customers, our business and the planet.
We're a team of more than 1,000 Trainliners from over 50 nationalities, working across London, Paris, Barcelona, Milan, Edinburgh and Madrid.
Now is a brilliant time to join us and help shape the future of travel.
Introducing the Embedded Data Engineering in ML Team
At the heart of our Data and ML teams, embedded Data Engineers create the pipelines and tables that power business critical dashboards, enable self-service analytics, and fuel advanced machine learning models and real-time data products.
Working with tools like DBT, Spark, and Airflow, you'll transform high volume raw event data into user-friendly, high impact datasets that support machine learning use cases across the business.
As an Embedded Data Engineer in ML, you'll sit within the Machine Learning team, working day to day with Machine Learning Engineers and Data Scientists to build reliable datasets for ML use cases.
You'll also have access to Trainline's wider Data Engineering, Data Platform, and analytics community, working alongside other embedded Data Engineers in ML, including senior and principal engineers.
- In this role as the Embedded Data Engineer (ML), you will
- Design and build scalable data pipelines, data models, and feature stores that support analytics and machine learning workloads within the ML domain.
- Deploy and maintain cloud-native data applications on AWS, using CI/CD pipelines to automate builds, testing, and releases.
- Maintain the technical quality, performance, and reliability of production data pipelines through strong observability and engineering best practices.
- Collaborate closely with Machine Learning Engineers and Data Scientists to build reliable, well-structured datasets that power ML use cases.
- Work with the wider Data Engineering, Data Platform, and analytics community to share knowledge and align on best practices across teams.
- We'd love to hear from you if you have
- Working knowledge of Python and SQL.
- Experience building data pipelines for downstream machine learning workloads, including feature engineering and model training workflows.
- Comfort with data modelling and building efficient data marts and warehouses in the cloud.
- Experience building data pipelines using tools such as Spark and Airflow, or similar technologies, within a cloud environment such as AWS.
- Familiarity with both real-time and batch data workloads, along with modern data transformation and orchestration patterns.
- Ideally, you may also have experience with parallel or distributed training frameworks such as Ray, or with modern data formats such as Parquet and Iceberg.
- It would also be helpful if you have some experience with Infrastructure as Code (Terraform) and containerisation (Docker) to support automated, standardised deployments.
- You may also have contributed to or maintained CI/CD pipelines (such as Jenkins or Git Hub Actions) as part of production grade data systems, and enjoy solving complex data problems collaboratively.
- More information
- private healthcare & dental insurance
- a generous work from abroad policy
- 2-for-1 share purchase plans
- an EV Scheme to further reduce carbon emissions
- extra festive time off
- 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!
We're operating a hybrid model and ask that Trainliners work from the office a minimum of 60% of their time over a 12-week period. We also have a 28-day Work from Abroad policy.
Our values represent the things that matter most to us and what we live and breathe everyday, in everything we do
- 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
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.
That's why we're committed to creating inclusive places to work, where everyone belongs and differences are valued and celebrated.
- Interested in finding out more about w
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Embedded Data Engineer - ML employer: Trainline
Trainline is an exceptional employer, dedicated to fostering a culture of innovation and sustainability in the travel industry. With a strong emphasis on employee growth, we offer clear career paths, personal learning budgets, and a supportive environment for mentorship and collaboration. Our hybrid work model, generous benefits, and commitment to diversity make Trainline a rewarding place to build a meaningful career while contributing to a greener future.
StudySmarter Expert Advice🤫
We think this is how you could land Embedded Data Engineer - ML
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Trainline!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Embedded Data Engineer - ML at Trainline.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Trainline.
✨Apply Directly through Our Website
When you find a suitable opening like Embedded Data Engineer - ML at Trainline, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Embedded Data Engineer - ML
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Trainline, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Trainline. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Trainline
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Trainline!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
