Data Engineer

Data Engineer

Full-Time 60000 - 80000 £ / year (est.) No working from home possible
Stint

At a Glance

  • Tasks: Own and evolve data pipelines, deploy ML systems, and maintain AWS-native data platform.
  • Company: Stint is revolutionising hospitality with AI and a flexible student workforce.
  • Benefits: Competitive salary, private medical insurance, office gym membership, and ownership shares.
  • Other info: Enjoy a dog-friendly office with free snacks and regular team meals.
  • Why this job: Join a fast-paced team and make a real impact in a growing start-up.
  • Qualifications: Technical background in Computer Science or related field; strong data engineering skills.

The predicted salary is between 60000 - 80000 £ per year.

At Stint, we’re using AI tools to transform how hospitality businesses operate. We started by building the UK’s largest flexible workforce of hyper-local students available for short 2–3 hour shifts, working with brands like PizzaExpress, Pret a Manger, Gails, and many more. Now, we’re combining that workforce with an AI tool – creating a platform that no competitor can replicate. In just 10 months, our new AI software has surpassed £1m in ARR and we are on track to multiply that in the next 12 months. We are aggressively capturing the UK market, and international expansion is next - with plans to launch big in the US soon.

We’re looking for a Mid / Senior Data Engineer to become a key technical backbone of our data platform. You’ll take ownership of the infrastructure and pipelines that power our analytics and machine learning - ensuring data flows reliably from ingestion through to production models and real-time decisioning. This is a hands-on role focused on building, shipping, and maintaining robust data and ML systems. You’ll work closely with Data Scientists, Engineers, and Product teams to keep our platform running smoothly while continuously improving reliability, scalability, and performance. We are an office-first, collaborative team and this role is based in Camden 3-4 days a week.

What you will be doing:

  • Own and evolve our data pipelines - designing, building, and maintaining ETL/ELT workflows that ensure high-quality, reliable data across the platform.
  • Deploy and support production ML systems - from forecasting models to LLM-driven and agentic workflows - with proper monitoring, versioning, and CI/CD.
  • Maintain and improve our AWS-native data platform (S3, Redshift, RDS, Athena, SageMaker, Lambda), driving operational best practices and platform reliability.
  • Write and optimise complex SQL to support analytics, reporting, and data modelling for downstream teams and BI tooling.
  • Build and maintain integrations with internal services and partner systems, ensuring clean and scalable data access.
  • Act as a go-to technical partner for production data issues - diagnosing problems, improving resilience, and keeping systems running smoothly.
  • Support and mentor junior team members through code reviews, pairing, and sharing best practices.
  • Work closely with stakeholders across the business to translate data challenges into practical, scalable solutions.

This position might suit you, if:

  • You’ve studied something technical - like Computer Science, Engineering, Mathematics, or a related field.
  • You have strong data engineering fundamentals, including pipeline design, orchestration, testing, and monitoring.
  • You’re confident writing production-quality Python and complex SQL across modern data platforms.
  • You have hands-on experience deploying and running ML systems in production, including model serving and monitoring.
  • You’re comfortable working across cloud infrastructure (ideally AWS) and understand platform and DevOps principles.
  • You’re pragmatic and ownership-driven - happy to dive into problems, unblock others, and keep things running.
  • You enjoy working in fast-moving environments where you can have real impact on how systems are built and scaled.

What we can offer you:

  • Salary is competitive and open to discussion based on level.
  • Private medical insurance.
  • A social, friendly and welcoming team based in the heart of Camden.
  • Office gym membership.
  • Ownership shares in a well-funded, growing start-up.
  • Dog friendly office!
  • Free office fruit and snacks.
  • Office dinner if working late.
  • Regular office breakfasts and lunches.

Data Engineer employer: Stint

At Stint, we pride ourselves on being an innovative employer that values collaboration and growth. Our office-first culture in the vibrant heart of Camden fosters a friendly atmosphere where you can thrive alongside a talented team, while our commitment to employee development ensures you have ample opportunities to enhance your skills and advance your career. With competitive salaries, private medical insurance, and unique perks like a dog-friendly office and regular team meals, we create a rewarding environment for our Data Engineers to make a meaningful impact in the hospitality sector.

Stint

Contact Details:

Stint Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Data Engineer

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 Stint!

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 Data Engineer at Stint.

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 Stint.

Apply Directly through Our Website

When you find a suitable opening like Data Engineer at Stint, 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 Data Engineer

Data Pipeline Design
ETL/ELT Workflows
AWS (S3, Redshift, RDS, Athena, SageMaker, Lambda)
SQL
Python
Machine Learning Systems Deployment
Monitoring and Versioning

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 Stint, 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 Stint. 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 Stint

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 Stint!

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.