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- Tasks: Transform raw data into impactful insights for AI products and business decisions.
- Company: Fast-growing healthcare scale-up focused on innovation and collaboration.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Join a dynamic team where your contributions directly impact patient outcomes.
- Why this job: Shape the future of healthcare with your data expertise and make a real difference.
- Qualifications: 3+ years in analytics engineering, strong SQL skills, and experience with modern data stacks.
The predicted salary is between 60000 - 80000 ÂŁ per year.
You will join our Data team as a senior analytics engineer modelling raw data and turning it into production ready tables for stakeholders and AI products that underpin the decisions that shape our business. This is a high‑impact role at the heart of a fast‑growing healthcare scale‑up — one where the data you model and the standards you set will directly influence how every team in the company operates, and how well our AI‑powered products perform. You'll work closely with data and business stakeholders. You will be the bridge between raw data and meaningful insight, ensuring our modern data stack — built on BigQuery, dbt and Looker — is scalable, trusted and AI‑ready. Your work will underpin both the analytical decisions of our internal teams and the performance of the patient‑facing AI tools we build. You'll have genuine ownership over the data models and governance practices that underpin our data function. At a company growing as fast as ours, the standards you establish today will shape how we scale tomorrow — across products, markets and millions of patient interactions.
What You'll Do
- Architect, model and optimise the core data models that power analytics and AI applications across the business, building for scale and performance from the ground up.
- Ensure the data layer is structured to support AI and LLM use cases — including feature pipelines, evaluation datasets and the clean, well‑documented data that reliable AI products depend on.
- Partner with cross‑functional teams across marketing, finance, operations and product to translate business requirements into robust, reliable technical solutions.
- Own data governance of the data models you own — ensuring integrity, consistency and security while maintaining documentation and enforcing best practices.
- Shape our data culture, driving adoption of rigorous modeling frameworks and analytical standards.
- Identify opportunities to improve the performance, reliability and usability of our data stack, and take full ownership of seeing those improvements through.
Who You Are
- AI & LLM awareness priority: A working understanding of how AI and LLM‑powered products consume data — including familiarity with feature engineering, evaluation pipelines and the data quality standards these systems require. Experience contributing to or supporting AI/ML workflows, whether through building feature stores, curating training data, or structuring outputs for model consumption.
- Technical expertise: 3+ years of experience in analytics engineering, data engineering or a closely related role. Advanced SQL skills — you can design, optimise and debug complex queries with confidence. Hands‑on experience with dbt or Dataform, and a strong track record of building scalable, well‑structured data models. Comfortable working within a modern data stack; direct experience with BigQuery and Looker is a strong advantage.
- Analytical acumen: Strong understanding of measurement approaches, data analysis and statistics — you think carefully about what a metric actually means before you build it. Able to hold both the technical and business context simultaneously, ensuring every data solution is anchored to a real company objective. Experienced in data governance, quality assurance and documentation — you understand that trusted data is the product.
How You Work
- A natural collaborator who can earn the trust of both data analysts and non‑technical stakeholders alike.
- Takes ownership end‑to‑end — from understanding a business problem to delivering a solution the whole company can rely on.
- Excited by the challenge of building in a fast‑paced environment and motivated by the idea that your work helps improve patient outcomes at scale.
- Someone who resonates with ownership, strategic thinking and the pace of a high‑growth scale‑up.
Senior Analytics Engineer employer: Manual
Contact Detail:
Manual Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Analytics Engineer
✨Tip Number 1
Network like a pro! Reach out to people in the industry, especially those already working at companies you're interested in. A friendly chat can open doors and give you insider info that could help you stand out.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your projects, especially those related to analytics engineering. This gives potential employers a taste of what you can do and how you think.
✨Tip Number 3
Prepare for interviews by practising common questions and scenarios specific to analytics engineering. Think about how you would explain complex data concepts to non-technical stakeholders — clarity is key!
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you're genuinely interested in joining our team.
We think you need these skills to ace Senior Analytics Engineer
Some tips for your application 🫡
Show Off Your Skills: When you're writing your application, make sure to highlight your technical expertise and experience in analytics engineering. We want to see how your skills with SQL, dbt, and data modelling can contribute to our data team.
Connect the Dots: Don’t just list your past roles; explain how they relate to the job at StudySmarter. Show us how your previous experiences have prepared you for this high-impact role and how you can bridge the gap between raw data and meaningful insights.
Be Authentic: Let your personality shine through in your application! We’re looking for someone who resonates with ownership and strategic thinking, so don’t be afraid to share your passion for data and how it can improve patient outcomes.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity to shape our data culture!
How to prepare for a job interview at Manual
✨Know Your Data Stack
Familiarise yourself with BigQuery, dbt, and Looker before the interview. Be ready to discuss how you've used these tools in your previous roles, especially in building scalable data models. This will show that you understand the technical requirements of the role.
✨Showcase Your Analytical Skills
Prepare examples of how you've approached data analysis and measurement in past projects. Highlight your ability to think critically about metrics and how they align with business objectives. This will demonstrate your analytical acumen and understanding of data governance.
✨Emphasise Collaboration
Since this role involves working closely with cross-functional teams, be prepared to share experiences where you've successfully collaborated with non-technical stakeholders. Discuss how you’ve translated complex data concepts into actionable insights for different teams.
✨Demonstrate Ownership and Initiative
Talk about instances where you've taken ownership of a project from start to finish. Highlight how you identified opportunities for improvement within your data stack and the steps you took to implement those changes. This will resonate well with the company's fast-paced, high-growth environment.