Data — Families — Roles and skills

Data analyst

Data analysts turn health and care data into information that helps clinicians, managers, and planners make decisions. They prepare, link, and analyse data from clinical, operational, and patient-reported sources, and present it in reports, dashboards, and briefings. They make sure their analysis is accurate, reproducible, and handles confidential patient data safely.

Performance analyst

Performance analysts measure how well the organisation's digital health services work for patients, service users, clinicians, and care staff. They set up measurement frameworks, collect and analyse usage and outcome data, and help product teams and service leaders understand whether changes improve access, experience, safety, and efficiency.

Data engineer

Data engineers build and run the pipelines and platforms that move health and care data from clinical and operational systems into places where it can be analysed and used safely. They design data flows, integrate sources that use different standards, and make sure data is accurate, timely, secure, and only available to the people who should see it.

Analytics engineer

Analytics engineers turn raw health and care data into clean, tested, and documented data models that analysts, data scientists, and reporting tools can rely on. They sit between data engineering and analysis, applying software engineering practices such as version control, testing, and code review to the transformations behind the organisation's reports and dashboards.

Data scientist

Data scientists use statistics, machine learning, and other advanced methods to find patterns in health and care data and to build models that help services plan, predict, and improve care. Typical work includes forecasting demand, identifying patients who may benefit from early support, and evaluating the effect of service changes. They work closely with clinicians and analysts, and make sure models are valid, fair, and used safely.

Machine learning engineer

Machine learning engineers take models built by data scientists, or bought from suppliers, and make them work reliably in live health and care services. They build the pipelines that train, test, deploy, and monitor models, and integrate them with clinical and operational systems. They make sure models stay safe, secure, fair, and effective once real patients and staff rely on them.

Band 7 Senior machine learning engineer
Band 8b Lead machine learning engineer

Data governance manager

Data governance managers make sure the organisation's health and care data is well managed, trustworthy, and used safely and lawfully. They set up and run the frameworks, policies, and standards for data ownership, quality, metadata, and data sharing, and work with data owners, information governance colleagues, and clinical leads so data can be used with confidence for care, planning, and research.

Clinical coder

Clinical coders read patients' health records and translate diagnoses, procedures, and other clinical information into standard classification codes, such as ICD for diagnoses and a procedure classification for interventions. Coded data is used for payment, service planning, clinical audit, research, and public health statistics, so its accuracy matters well beyond the coding team. Coders work closely with clinicians to clarify records, and increasingly use clinical terminologies such as SNOMED CT and computer-assisted coding tools.

Band 3 Trainee clinical coder
Band 4 Clinical coder
Band 5 Senior clinical coder
Band 6 Clinical coding auditor and trainer
Band 7 Clinical coding manager