Data Quality and Governance Officer

City of London
3 months ago
Applications closed

Related Jobs

View all jobs

Data Quality Officer

Head of Business Intelligence & Reporting

Head of Data Analytics and Transformation IH

Data Analytics Engineer II

Data Analytics Engineer II

Data Analytics Engineer II

Tempest Charities are recruiting for a Data Quality and Governance Officer, based anywhere in UK, Hybrid.

Salary up to £39K. You will have experience of working as a Data Analyst but have the desire to expand your skills, the Organisation is going through a Digital Transformation.

Data Quality & Governance Experience

Conducted IT data audits and created business process models to identify personal data usage
Performed data cleansing activities and managed data quality across multiple systems
Led GDPR implementation, directly aligning with the role's compliance requirementsTechnical Proficiency

Extensive experience querying databases and producing statistical reports
Managed defect logs and testing processes, demonstrating systematic quality control
Configured and customised CRM systems with validation rules and data standardsDocumentation & Standards

Produced comprehensive policy documents and compliance artefacts for executive approval
Created and maintained Requirements Traceability Matrices
Developed functional design documents and facilitated stakeholder sign-off sessionsStakeholder Engagement

Facilitated workshops to capture processes and gather requirements across diverse stakeholders
Worked across Financial Services, Telecoms, Oil, Motoring and Charity sectors
Collaborated with both technical teams and non-technical business usersCharity Sector Knowledge

Understands the unique data and reporting challenges facing charitable organisationsProcess Improvement

Designed formal change request processes and created "As-is" and "To-be" process diagrams
Supported multiple change programmes with improved governance and delivery tracking
Experience with system migrations, testing, and UAT supportExperience:

Data Analysis
Fundraising CRM
Reporting, importing complex data
Fundraising CRM

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.