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- Tasks: Design and maintain data pipelines for healthcare AI, ensuring data quality and reliability.
- Company: Join a pioneering team reshaping the healthcare industry with innovative technology.
- Benefits: Enjoy remote work flexibility, competitive salary, and opportunities for professional growth.
- Other info: Be part of a dynamic team where your contributions truly matter.
- Why this job: Make a real impact in healthcare by building cutting-edge data solutions.
- Qualifications: 7+ years in data engineering, proficient in Python, and experienced with large datasets.
The predicted salary is between 48000 - 72000 £ per year.
Location: UK or Europe | Remote (±2–3 hrs GMT overlap mandatory)
Reports to: Head of Engineering
Existing Clients: Top 100 Lifesciences, MedTech and Pharma companies
Type: Full-time
Core responsibilities & objectives
- Design, build, and maintain batch/streaming data pipelines, ingestion, cleaning, normalisation, enrichment, deduplication.
- Build and own ML/LLM pipelines end-to-end: document parsing, chunking, embeddings generation, vector indexing, agentic tool calling, multi-step workflows, retries, fallbacks, and state handling.
- Write production-grade, well-tested Python that processes large volumes of data and documents reliably.
- Own pipeline health: if data is stale, broken, or wrong, it's on you.
- Work autonomously to project deadlines with minimal hand-holding.
Key qualifications & skills (non-negotiable)
- 7+ years in backend data-heavy development or data engineering.
- Highly proficient in Python.
- Hands-on experience with large datasets and high-velocity data streams (Kafka, Flink, Spark).
- Strong with pipeline orchestration tools (Airflow, MLflow, or equivalent).
- Solid SQL skills (Postgres, BigQuery, or Snowflake) and NoSQL experience (DynamoDB, OpenSearch, Elastic).
- Real experience with LLM workflows: RAG architectures, embeddings/vector DBs, prompt engineering, function/tool calling, observability.
- Deep understanding of ETL/ELT patterns and data processing at scale.
Preferred background (strong signals)
- Experience with AWS data stack at scale.
- Exposure to healthcare, life sciences, or regulated industries.
- Built and shipped data, ML and LLM-powered pipelines in production.
- Has debugged a pipeline and knows why observability matters.
- Worked in a fast-moving startup where "that's not my job" doesn't exist.
What will get you rejected
- "I set up the pipeline, someone else monitors it" mindset.
- Tutorials and side projects but no production experience at scale.
- Can't explain trade-offs between streaming vs. batch, or why you chose one vector DB over another.
- Needs detailed specs before writing a line of code.
- No curiosity about healthcare or what the data actually means.
Interested? We're a distributed team solving hard problems that will reshape the healthcare industry for a generation. If you want ownership, not just tickets, we'd like to hear from you.
Senior Data Engineer – Healthcare AI employer: Vamstar
Contact Detail:
Vamstar Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Data Engineer – Healthcare AI
✨Tip Number 1
Network like a pro! Reach out to folks in the healthcare AI space on LinkedIn or at industry events. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data pipelines and ML projects. When you apply through our website, include links to your work so we can see what you're capable of.
✨Tip Number 3
Prepare for the interview by brushing up on your knowledge of ETL/ELT patterns and the tools we use. We love candidates who can discuss their thought process and trade-offs in depth.
✨Tip Number 4
Be curious! Show us your passion for healthcare and data. Ask insightful questions during interviews to demonstrate your interest in the field and the role.
We think you need these skills to ace Senior Data Engineer – Healthcare AI
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in backend data-heavy development and showcases your Python skills. We want to see how your background aligns with the responsibilities of designing and maintaining data pipelines.
Showcase Your Projects: Include specific examples of projects where you've built and shipped data or ML pipelines. We love seeing real-world applications, so don’t hold back on the details about your production experience!
Be Clear and Concise: When writing your cover letter, get straight to the point. Explain why you’re a great fit for the Senior Data Engineer role and how your skills can help us tackle the challenges in healthcare AI.
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 don’t miss out on any important updates from our team!
How to prepare for a job interview at Vamstar
✨Know Your Data Inside Out
Make sure you understand the intricacies of data pipelines and the specific technologies mentioned in the job description. Brush up on your knowledge of Python, SQL, and any relevant tools like Kafka or Airflow. Being able to discuss your past experiences with these technologies will show that you're not just familiar but truly proficient.
✨Showcase Your Problem-Solving Skills
Prepare to discuss real-world scenarios where you've had to debug a pipeline or handle data issues. Be ready to explain your thought process and the steps you took to resolve the problems. This will demonstrate your hands-on experience and your understanding of why observability is crucial in data engineering.
✨Understand the Healthcare Context
Since this role is focused on healthcare AI, take some time to research the industry. Familiarise yourself with common challenges and trends in healthcare data management. Showing genuine curiosity about how data impacts healthcare can set you apart from other candidates.
✨Be Ready for Technical Questions
Expect technical questions that assess your understanding of ETL/ELT patterns and the trade-offs between streaming and batch processing. Prepare to explain your choices in past projects, especially regarding vector databases and LLM workflows. This will highlight your depth of knowledge and ability to think critically about data engineering decisions.