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Data Engineer
Data Engineer at Abtrace
We're solving complex healthcare challenges with intelligent analysis. Our mission is to drive meaningful improvements for patients and healthcare providers by making healthcare more proactive, preventative, and efficient.
About Abtrace
The NHS faces unprecedented pressure as primary care teams manage an increasing patient load with limited resources. Our mission is to help healthcare be more proactive and streamlined by delivering automated interventions like measurements, vaccinations, and reviews.
With over 500 supported UK practices serving 6 million people, we automate routine care, improve health outcomes, and elevate experiences for both staff and patients. Our success relies on rethinking how healthcare interactions happen – with intelligent systems managing the repetitive, leaving skilled professionals to focus on what matters most.
Healthcare professionals deserve software that is reliable, safe, modern, thoughtful, and well designed.
Strong data infrastructure and deep analytics ensure we understand what works, identify improvement opportunities, and continually refine our tools.
Role Overview
We are hiring our first dedicated Data Engineer to establish the foundation of our analytics and product teams.
Currently, our data comes from a mix of warehouse tables and external sources as our company evolves. As we scale, we need a skilled Data Engineer to organise this into a clean, centralised, well-governed data platform – reliable, easy to access, and trusted across the organisation.
This is a senior individual-contributor role, giving you hands-on responsibility for writing pipelines, building models, and defining the architectural direction of our data systems.
Ideally, your experience includes transitioning from nascent to sophisticated data infrastructures, ensuring you know what effective data systems look like.
Key Responsibilities
Data Pipeline Design & Development
- Design, build, and maintain data pipelines that ingest from varied sources:
- Third-party APIs
- Operational databases
- File-based exports
- Primary language: Python on AWS
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Data Warehouse & Analytics Foundation
- Own and evolve our data warehouse architecture, identifying structured approaches to scale, enhance reliability, and manage aspects as code
- Transition to a more unified framework, consolidating data flows into a cleaner, more maintainable format
Data Release & Operational Efficiency
- Build a fast and safe path from “request for data” to “ready for analysts and business use.”
- Currently: reliable but slow release process
- Future: streamlined testing, seamless integration, and efficient model updates
Transformation & Orchestration
- Implement transformation tooling using dbt with versions, testing tools, and code review capabilities
- Integrate orchestration systems such as Airflow, Dagster, or Step Functions
Access & Quality Controls
- Enable secure, unreliable access to data for engineering, analytics, and product teams
- Establish controls aligned with healthcare compliance
- Ensure auditability of all data access
Monitoring & Governance
- Implement data quality checks, monitoring, and alerting across critical pipelines
Team Collaboration & Growth
- Partner with analytics, engineering, and product teams to optimize workflows:
- Conduct code reviews, mentorship on engineering best practices, and improve developer experience
- Collaborate with team members across clinical and operational stakeholder domains
Security & Compliance
- Contribute to data security and compliance with ISO 27001 and GDPR
- Align practices with healthcare regulations
Long-term Strategy
- Help shape longer-term data platform strategy as the company grows
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Requirements
Essential
- Extensive production experience as a Data Engineer or Backend Engineer, specializing in data systems
- Strong skills in:
- SQL (complex and real-time applications)
- Python for data processing
- Proven design and operational expertise in:
- Data pipelines (ingestion, transformation, and orchestration)
- Hands-on knowledge of cloud data platforms, especially AWS:
- Experience ‘standing up’ data warehouses/lakes such as Redshift, Snowflake, Databricks, or BigQuery
- Familiarity with modern transformation tools like dbt and orchestration (e.g., Airflow, Dagster, Step Functions, etc)
- Infrastructure-as-code expertise:
- Techniques like Terraform, CloudFormation, CDK
- Ability to lead architecture decisions and manage complex evolving systems
- Collaborative and communicating across team boundaries, especially with non-technical and healthcare stakeholders
Nice-to-Have
- Experience in healthcare or regulated industries:
- Familiarity with ISO 27001, GDPR, or HIPAA
- Experience with data governance at scale, including:
- Data classification, masking, fine-grained permissions, audit trails
- Background in early-stage roles like data engineer in a startup
- Expertise in data quality, observability, or platform engineering
Benefits
Competitive Package
- Salaries/Bonuses to reflect the strategic impact of the role
Meaningful Work
- Opportunity to shape healthcare outcomes through innovative data solutions
Organisational Culture
- Collaborative yet autonomous working environment encouraging learning and innovation
Ongoing Support
- Professional development opportunities to explore emerging data technologies
Flexibility
- (partially) remote work schedule, with a balanced approach to work-life integration
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