NHS - Data Engineer Band 6

Gloucester
11 months ago
Applications closed

Related Jobs

View all jobs

Healthcare Workforce Data Analyst – ESR & BI Insights

Data Analyst

Remote Healthcare Data Analyst — Power BI & SQL

Data Scientist (Predictive Modelling) – NHS

Data Scientist (Predictive Modelling) – NHS

CHIS IIU Data Analyst Specialist | Kent Community Health NHS Foundation Trust

Exciting Opportunity: Permanent Band 6 NHS Data Engineer – £37,338 to £44,962 per year

PLEASE NOTE THIS ROLE WILL REQUIRE ONE DAY A WEEK ONLY ON SITE IN GLOUCESTER CITY CENTRE, THE REMAINDER OF THE ROLE CAN BE WORKED REMOTELY /HOME.

Are you a data-driven professional with a passion for leveraging technology to improve healthcare services? Join our forward-thinking Business Intelligence (BI) team as a Band 6 Data Engineer, where you’ll play a crucial role in shaping the data landscape of the NHS in Gloucestershire. This is a fantastic opportunity to work in a dynamic environment, collaborating with experts across digital teams, data service providers, and NHS organisations.

Why Join Us?

  • Make a Meaningful Impact: Your work will directly contribute to enhancing healthcare delivery, improving patient outcomes, and streamlining data-driven decision-making.

  • Cutting-Edge Technology: Work with cloud platforms like AWS and Microsoft, utilizing SQL, SSIS, Power BI, and other advanced tools to develop high-quality data solutions.

  • Collaborative & Supportive Team: Be part of a skilled team of professionals within a larger Business Intelligence department, where knowledge sharing and professional growth are valued.

  • Career Growth & Development: Benefit from continued professional development in a specialist area, gaining experience with emerging technologies like Python, R, and cloud-based computing.

    Key Responsibilities

  • Design & Maintain Data Pipelines: Develop efficient data ingestion solutions and integrate complex datasets to support business intelligence and reporting needs.

  • Enhance NHS Data Architecture: Support data quality improvements, maintain data warehouses, and ensure compliance with NHS data security and governance standards.

  • Automate & Streamline Reporting: Develop self-service reporting solutions to empower NHS staff with actionable insights.

  • Problem-Solving & Innovation: Collaborate with NHS partners and third-party data providers to troubleshoot, refine, and enhance data systems.

  • Data Security & Governance: Ensure adherence to GDPR, Caldicott principles, and Data Protection guidelines while managing data flows and improving data integrity.

    What experience you will need?

  • 1 years + experience working in a data / information analyst engineer role Experience in SQL (SSIS) and Power BI.

  • Desirable to have a data / compputer sceince degreee but not essential

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.

Careers Jobs

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

Jobs

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Education Jobs

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.