At a Glance
- Tasks: Build data models and pipelines to empower analysts with reliable insights.
- Company: Join the UK's leading premium publisher with iconic media brands.
- Benefits: Enjoy health perks, flexible work options, and exclusive discounts.
- Other info: Collaborative culture focused on creativity and innovation.
- Why this job: Make a real impact by transforming data into actionable insights.
- Qualifications: 2-4 years in analytics engineering, proficient in SQL and Python.
The predicted salary is between 28800 - 42000 £ per year.
We are the UK's leading premium publisher, and the people behind iconic media brands such as Cosmopolitan, Esquire, ELLE, Harper's Bazaar and Country Living. We have created a working environment that encourages everyone to pull together. We never stop looking for brave new ideas. We will always try to develop and improve, we trust each other to do our best, and we have fun as we build on our heritage to make history.
Please note: This role is office based 4 days a week.
The Role… As the Analytics Engineer you will play a key role in helping the business understand our customers. Sitting between data engineering and analytics, you'll be the person who empowers analysts with clean, reliable, and accessible data, building the datasets, pipelines, and tools they need to explore insights, while also enabling and contributing to more advanced analytics such as RFM scoring, segmentation, and predictive modelling.
Main Duties…
- Build and maintain data models and pipelines within our GCP data environment to make data accessible, reliable, and usable for analysts and stakeholders.
- Collaborate with Merkle engineers (who maintain our GCP-based data lake) to ensure data lake architecture supports evolving analytical and reporting needs.
- Work with analysts to understand their data requirements, designing scalable solutions that support dashboards, customer metrics, and campaign analytics.
- Support advanced analytics projects, including RFM scoring, customer segmentation, and predictive models that help inform marketing and retention strategies.
- Optimise data workflows to improve performance, reduce duplication, and support self-serve analytics through tools like Looker and our CDP, ActionIQ.
What We Are Looking For…
- At least 2–4 years of experience in a data or analytics engineering role.
- Proficient in SQL and experienced working with large datasets within GCP (BigQuery) or similar cloud environments.
- Experience with Python for data transformation and automation.
- Experience with code management, review and reversion using GitHub.
- Comfortable collaborating with both analysts and engineers, translating business needs into robust technical solutions.
- Familiarity with Looker (or another BI tool) and an understanding of how to structure data for efficient dashboarding and self-serve analytics.
- Exposure to customer analytics concepts such as segmentation, LTV, churn, and RFM analysis is a plus.
- An interest in predictive analytics or data science (experience in predictive modelling or machine learning would be a bonus).
- Strong communicator who enjoys helping others make the most of data.
Benefits… Your benefits at Hearst UK are more than just extras—they are tools to help you thrive in every part of life.
- Inclusion, Health & Wellbeing: Stay healthy with Specsavers eye care, a company-funded Health Cash Plan, and access to mental health support. Get active and stress-free with discounted gym memberships and the Cycle to Work scheme. Embrace flexibility with a Location Flex and Holiday Exchange to take time when you need it. Take time to give back with a Charity Day and access wellbeing resources whenever you need them.
- Financial Wellness: Plan ahead with a generous Workplace Pension, Income Protection, Life Assurance and Season Ticket Loan for easier commuting. Make smarter money moves using Salary Finance tools, Financial Wellbeing sessions, and Home Tech benefits to spread costs. Treat yourself with major discounts across London plus everyday savings via the HAPI at Hearst app.
Hearst UK is deeply committed to using our influential brands to reflect the world we want to live in – one that respects, protects, represents and uplifts the voices and opinions of all people. As a business, we recognise the significant benefits of creativity, collaboration and innovation that comes with diverse teams. Not only is diversifying the voices in our organization the right thing to do, but it also helps us to make powerful and exciting content that can be enjoyed by many more people. This is why we're working to build a sense of true belonging within our business and foster a culture in which everyone feels heard.
Analytics Engineer in London employer: Hearst
At Hearst UK, we pride ourselves on being an exceptional employer, offering a vibrant work culture that fosters creativity and collaboration. As an Analytics Engineer, you'll benefit from our commitment to employee growth through access to advanced analytics projects and a supportive environment that values your contributions. With a range of health and wellbeing benefits, flexible working options, and a focus on diversity and inclusion, you'll find a rewarding career path in the heart of London, surrounded by iconic media brands.
StudySmarter Expert Advice🤫
We think this is how you could land Analytics Engineer in London
✨Tip Number 1
Network like a pro! Reach out to current employees on LinkedIn or attend industry events. A friendly chat can give us insights into the company culture and maybe even a referral!
✨Tip Number 2
Prepare for the interview by brushing up on your SQL and Python skills. We want to see you confidently tackle technical questions, so practice coding challenges and be ready to discuss your past projects.
✨Tip Number 3
Show off your passion for data! During interviews, share examples of how you've used data to drive decisions or improve processes. We love hearing about your creative problem-solving skills.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, it shows us you’re genuinely interested in joining our team.
We think you need these skills to ace Analytics Engineer in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that match the Analytics Engineer role. Highlight your proficiency in SQL, GCP, and any relevant projects you've worked on. We want to see how you can contribute to our team!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Share your passion for data and analytics, and explain why you're excited about working with us at Hearst UK. Let us know how your background aligns with our mission and values.
Showcase Your Projects:If you've worked on any cool data projects, don't hesitate to mention them! Whether it's building data pipelines or advanced analytics, we love seeing real examples of your work. It helps us understand your capabilities better.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you're serious about joining our team!
How to prepare for a job interview at Hearst
✨Know Your Data Inside Out
As an Analytics Engineer, you’ll need to demonstrate your proficiency in SQL and GCP. Brush up on your skills and be ready to discuss how you've built data models or pipelines in the past. Prepare examples that showcase your ability to make data accessible and reliable for analysts.
✨Collaborate Like a Pro
This role requires working closely with both analysts and engineers. Think about times when you’ve successfully collaborated on projects. Be prepared to share how you translated business needs into technical solutions, and show that you can communicate effectively across teams.
✨Showcase Your Problem-Solving Skills
Expect questions that assess your ability to optimise data workflows and support advanced analytics projects. Prepare to discuss specific challenges you’ve faced in previous roles and how you overcame them, especially in areas like RFM scoring or customer segmentation.
✨Be Ready for Technical Questions
You might be asked to solve a problem on the spot or explain your thought process regarding data transformation using Python. Practise common technical interview questions related to data engineering and be ready to demonstrate your coding skills, especially with GitHub.