MDM Manager

Reading
11 months ago
Applications closed

Related Jobs

View all jobs

Data Governance Manager (Inside IR35)

Senior Manager Business Intelligence

Senior Business Intelligence Manager

Senior Business Intelligence Manager

Senior Business Intelligence Manager

Data Governance Manager

Master Data Manager

£80,000 - £85,000 (+car allowance: £5,800, bonus, pension, private health care)

Mentmore are working with a leading household name to secure a Master Data Manager.

Acting as a senior expert in MDM content, processes, and procedures.
Overseeing the establishment of a golden master record for all data assets, ensuring a single source of truth.
Advocating for and implementing MDM best practices.
Developing and implementing the MDM strategy and framework.
Setting up MDM processes to support data governance and stewardship.
Leading the implementation of business rules, overseeing data governance activities, and managing MDM data mapping and ingestion, ensuring the system aligns with the organization's data strategy.
Ensuring the quality of data in the enterprise data platform, implementing data governance practices, data validation processes, and other measures to ensure data accuracy and consistency.
Maintaining data quality and uniformity across diverse systems.
Addressing and resolving issues related to conflicting data ownership, data and rule definition, and data availability.
Implementing business rules and data governance activities within the MDM system.

Team Management

Demonstrated expertise in leading a Master Data Management team, providing guidance and support, and ensuring the achievement of team objectives. This includes fostering a collaborative environment, mentoring team members, and driving continuous improvement in master data management practices.

Aligning with Business Objectives

Ensure MDM solutions support the organization's business objectives, working closely with the Head of Data Management and Governance and other stakeholders.
Leading Master Data Projects: Partner with IT and business stakeholders to lead complex, cross-functional master data projects.
Collaborating with IT and Business Teams: Work closely with IT teams to oversee the implementation of the data solution, and with business teams to understand their data needs.
Implementing MDM Program: Activate and enforce the master data management program vision, promote the role of MDM, ensure adoption, and monitor and manage data quality within the MDM program, working with data owners and stewards to address any issues

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.