Data Governance Analyst

Experis
London
2 months ago
Applications closed

Related Jobs

View all jobs

Data Governance Analyst (PIM)

Data Governance Analyst, Data Owner, Data Business Analyst,City London

Data Governance Analyst, Data Owner, Data Business Analyst, City of London

Data Governance Analyst Manchester Hybrid

Data Governance Analyst

Data Governance Analyst

Data Governance Analyst
Location: Milton Keynes
Duration: 31/12/2026
Days on site: 2
Rate £414
MUST BE PAYE THROUGH UMBRELLA
Role Description:
We are seeking a highly skilled Data Governance Analyst to support our data governance initiatives across business units in the car finance domain. This role is critical in ensuring that data governance policies are effectively implemented and aligned with business needs. The successful candidate will be deeply familiar with data governance frameworks and possess hands-on experience with Collibra's Data Intelligence Platform or similar leading market data governance product.
Key Responsibilities
Partner directly with business units to understand operational data needs and ensure alignment with governance policies.
Support the development, implementation, and maintenance of data governance standards, policies, and procedures.
Promote data ownership and stewardship across departments, ensuring accountability and compliance.
Leverage Collibra to manage data domains, workflows, and governance artifacts.
Facilitate data quality initiatives, including issue resolution and root cause analysis.
Maintain metadata repositories and ensure consistent data definitions across systems.
Monitor regulatory compliance and support audit and risk management activities.
Provide training and guidance to business users on data governance principles and Collibra usage.
Required Skills & Experience
5+ years of experience in data governance, data management, or related roles.
Strong understanding of data governance frameworks (e.g., DAMA-DMBOK).
Strong Business & data analyst skills
Experience in Collibra or similar tooling, including cataloging, policy management, and workflow configuration.
Excellent interpersonal and communication skills for engaging with business stakeholders.
Knowledge of data privacy regulations (e.g., GDPR) and financial services compliance.
Analytical mindset with attention to detail and problem-solving capabilities.

TPBN1_UKTJ

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.