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- Tasks: Design and build scalable data pipelines for impactful healthtech projects.
- Company: Join a pioneering healthtech platform transforming clinical research with real-world data.
- Benefits: Enjoy 25 days holiday, private healthcare, and flexible working options.
- Why this job: Make a difference in healthcare by turning complex data into actionable insights.
- Qualifications: 7+ years in data engineering, strong SQL skills, and AWS experience required.
- Other info: Be part of a dynamic team with opportunities for mentorship and career growth.
The predicted salary is between 36000 - 60000 £ per year.
About uMed: uMed is a healthtech and data platform advancing clinical research through high-quality real-world and patient-generated data. uMed combines RWE with the power of patient-generated data to address the evidence gaps in life science research. We build secure, scalable data platforms that transform complex patient, survey, and clinical data into actionable insights for researchers, clinicians, and partners.
As a Senior Data Engineer, you will be responsible for designing, building, and operating uMed’s core data pipelines, data warehouse layers, and analytics-ready datasets. During this phase of growth, you will be the primary Data Engineer, requiring a high degree of autonomy, technical judgement, and ownership. You will also act as line manager for a mid-level Data Engineer, providing hands-on technical mentorship, day-to-day guidance, and code review, while remaining deeply involved in data pipeline delivery.
You will work in close partnership with the Product Architect, Enterprise Data, who owns the enterprise data models and long-term data design, and collaborate closely with the Data Platform Engineer, who owns cloud infrastructure, environments, and DevOps. You will support data across the UK and US regions, ensuring data is reliable, compliant, and fit for analytics, insights, and AI-driven use cases.
Responsibilities
- Data Engineering & Pipelines: Design, build, and maintain scalable, reliable data pipelines for structured and semi-structured data. Own end-to-end delivery of ETL/ELT pipelines from source ingestion through to analytics-ready outputs. Ensure pipelines are performant, cost-efficient, observable, and production-grade.
- Data Modelling & Analytics Enablement: Partner closely with the Product Architect on data modelling and design, contributing implementation expertise and feedback from real-world usage. Translate canonical and logical data models into performant physical models in the analytics warehouse. Implement transformations that support cross-study, cross-drug, and cross-region analytics and comparisons. Deliver reusable, well-documented datasets aligned with a Data as a Product approach.
- Data Quality, Governance & Compliance: Implement data quality checks, validation rules, and reconciliation processes across data sources. Ensure datasets and pipelines comply with regional data protection and regulatory requirements. Maintain clear documentation, data lineage, and auditability in line with enterprise standards.
- Cloud & Platform Collaboration: Build and operate data pipelines on AWS. Partner closely with the Data Platform Engineer to align data pipelines with platform infrastructure, environment configuration, access controls, and deployment patterns. Collaborate on well-scoped data development tasks where shared context or operational efficiency makes sense, while maintaining clear ownership boundaries.
- Analytics, Insights & AI Enablement: Work with Analytics and Product teams to enable dashboards, reporting, and insight generation. Ensure data structures support dynamic filtering, comparisons, and narrative or AI-driven insights. Support downstream AI/ML and insight-generation use cases with clean, well-structured data.
- Collaboration & Technical Leadership: Act as a senior technical contributor and mentor as the data function grows. Line manage and mentor a mid-level Data Engineer, supporting their technical growth and ensuring high-quality delivery. Promote best practices in data engineering, testing, documentation, and code quality. Communicate clearly with the product team and relevant engineering stakeholders to translate requirements into robust data solutions.
Required Qualifications
- 7+ years of experience in data engineering or closely related roles, with demonstrated ownership of production data systems.
- Strong SQL skills and deep experience implementing analytics-focused data models.
- Proven experience building and operating production data pipelines on AWS.
- Experience working with complex or semi-structured data.
- Strong understanding of data quality, reliability, and production-grade data systems.
- Experience mentoring or line managing engineers in a hands-on, technical role.
Technical Stack (Experience With Some of the Following)
- AWS: S3, Redshift, RDS, Athena, DocumentDB (or equivalent).
- Data transformation tools (e.g., dbt or similar).
- Orchestration tools (e.g., Airflow, Step Functions, or similar).
- Python (or similar) for data processing and automation.
- BI tools and analytics enablement (e.g., Zoho Analytics, Tableau, Power BI, or similar).
Nice to Have
- Experience in healthcare, clinical research, or regulated data environments.
- Experience working with multi-region data platforms (UK and US).
- Familiarity with AI/ML data pipelines or insight-generation workflows.
- Experience supporting data products for external customers or partners.
25 days’ holiday, plus your birthday off and bank holidays on top. Private healthcare through Vitality. 2 paid volunteering days per year. A monthly allowance to spend via our flexible benefits portal. One day a week working from a coworking space of your choice, if you want to mix up working from home. Equipment setup allowance to make your home office work for you. Enhanced maternity, paternity, and parental leave.
Senior Data Engineer employer: uMed
Contact Detail:
uMed Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Data Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data engineering projects, especially those that highlight your experience with AWS and data pipelines. This will give potential employers a taste of what you can do.
✨Tip Number 3
Prepare for interviews by brushing up on your technical skills and understanding the latest trends in data engineering. Practice common interview questions and be ready to discuss your past projects in detail.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are genuinely interested in joining our team at uMed.
We think you need these skills to ace Senior Data Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Senior Data Engineer role. Highlight your experience with data pipelines, AWS, and any relevant projects that showcase your skills. We want to see how your background aligns with what we're looking for!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about healthtech and how your expertise can contribute to uMed's mission. Keep it concise but impactful – we love a good story!
Showcase Your Technical Skills: Don’t hold back on showcasing your technical skills in your application. Mention specific tools and technologies you've worked with, especially those listed in the job description. We’re keen to see your hands-on experience!
Apply Through Our Website: We encourage you to apply through our website for a smoother process. It helps us keep track of applications and ensures you get all the updates directly from us. Plus, it’s super easy!
How to prepare for a job interview at uMed
✨Know Your Data Inside Out
As a Senior Data Engineer, you'll be expected to have a solid grasp of data engineering principles. Brush up on your SQL skills and be ready to discuss your experience with building and operating production data pipelines, especially on AWS. Prepare examples that showcase your ownership of data systems and how you've tackled challenges in the past.
✨Showcase Your Mentorship Skills
Since you'll be line managing a mid-level Data Engineer, it's crucial to demonstrate your leadership abilities. Think of specific instances where you've mentored others or led a team. Be prepared to discuss your approach to technical mentorship and how you ensure high-quality delivery from your team.
✨Understand the Compliance Landscape
Given the importance of data quality and compliance in healthtech, make sure you're familiar with regulations like UK GDPR and US HIPAA. Be ready to talk about how you've implemented data quality checks and validation rules in your previous roles, ensuring that your datasets are reliable and compliant.
✨Collaborate Like a Pro
Collaboration is key in this role, so think about how you've worked with cross-functional teams in the past. Prepare to discuss your experiences partnering with product architects or data platform engineers, and how you’ve aligned data pipelines with infrastructure needs. Highlight your communication skills and ability to translate complex requirements into actionable data solutions.
