EPR Data Quality Manager

Brio Digital
City of London
1 month ago
Applications closed
EPR Data Quality Manager

Brio Digital. Greater Bristol Area, United Kingdom (Remote)


Sector: Public Health. Location: Primarily Remote with occasional on-site need. Start: ASAP.


Overview

We are seeking an experienced EPR Data Quality Manager to support a public sector / NHS organisation in improving, maintaining, and governing the quality of Electronic Patient Record (EPR) data. This role will play a critical part in ensuring data accuracy, completeness, and compliance with NHS data standards, enabling safe clinical care, effective reporting, and informed decision‑making.


The ideal candidate will have hands‑on experience working with NHS EPR systems, strong technical data skills, and a deep understanding of healthcare data models and standards.


Key Responsibilities

  • Lead and manage data quality initiatives across EPR systems.
  • Define, monitor, and improve data quality metrics and standards.
  • Work closely with clinical, operational, and digital teams to resolve data quality issues.
  • Develop and maintain data validation rules and reconciliation processes.
  • Ensure EPR data aligns with NHS data standards and statutory reporting requirements.
  • Support reporting, analytics, and downstream system integrations.
  • Provide subject matter expertise on RTT rules, data models, and patient pathway data.
  • Investigate and remediate data quality issues at source.
  • Produce clear dashboards and reports to highlight data quality risks and improvements.
  • Support data governance, audits, and assurance activities.

Essential Skills & Experience

EPR & NHS Experience



  • Proven experience working with NHS EPR systems (vendor‑agnostic).
  • Strong understanding of NHS data flows and healthcare data models.
  • In‑depth knowledge of NHS data standards and governance principles.
  • Experience applying RTT rules and managing RTT data models.

Technical Skills



  • Advanced SQL skills for data interrogation, validation, and reporting.
  • Strong Excel skills, including complex formulas, data validation, and analysis.
  • Experience building reports and dashboards using Power BI.
  • Working knowledge of Oracle databases.

Interoperability & Standards



  • Experience working with healthcare messaging and interoperability standards, including HL7, FHIR.
  • Knowledge of clinical coding standards, including SNOMED CT, ICD‑10.

Desirable Skills

  • Experience working in a data quality or data governance leadership role.
  • Understanding of secondary uses data and statutory returns.
  • Experience working in complex, multi‑stakeholder NHS environments.
  • Ability to translate technical data issues into clear, non‑technical language.

Apply now or email for more information.


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.