Senior Data Engineer

About us

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.

Requirements

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 (which will also be a new role), 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.

Architectural ownership of enterprise data models sits with the Product Architect, Enterprise Data, while the Senior Data Engineer owns the implementation, reliability, and evolution of data pipelines and analytics-ready datasets.

Responsibilities

Data Engineering & Pipelines

• Design, build, and maintain scalable, reliable data pipelines for structured and semi-structured data (e.g. surveys, EHR extracts, events, and operational systems).
• 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, Enterprise Data, 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 (UK and US) 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 (e.g., UK GDPR, US HIPAA, where applicable).
• Maintain clear documentation, data lineage, and auditability in line with enterprise standards.

Cloud & Platform Collaboration

• Build and operate data pipelines on AWS (e.g., S3, Redshift, RDS, Athena, DocumentDB).
• 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 (e.g., Zoho Analytics or equivalent BI tools).
• 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

Core Requirements

• 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 (e.g., nested JSON, event 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.

Benefits

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