SAP MM Data Expert

Luton
10 months ago
Applications closed

Related Jobs

View all jobs

SAP Master Data Analyst

Data Architect

Data Architect

Data Architect

Purchasing Data Quality Support Assistant

Senior Business Intelligence Manager

SAP MM Data Expert

Luton - Hybrid - £650 - 6 months

The SAP MM Data Expert is responsible for supporting the definition of business data requirements within the S/4 HANA design process, defining and documenting the S/4 Enterprise Data Standards, and ensuring that existing ECC data is fit for purpose at the point of migration to S/4 HANA for a defined group of data objects/processes.

This is not a migration role

The role is aligned to Procurement & MM and is responsible for proactively engaging with the wider business (including data offices and governance forums) and other relevant partners to ensure that the S/4 data design meets business requirements, aligns to SAP standard where possible and that S/4 data can be used with confidence achieving a Quality Core.

Skills for SAP Data MM Expert

Significant experience and domain expertise in P&MM. Proven knowledge of how business data requirements support process execution and analytics, with the ability to explain complex data concepts to business users.

Demonstrable experience of designing and implementing Data Standards for a global enterprise with significant geographical and functional footprint.

SAP solid understanding across transactions and reporting in an SAP environment, including an understanding of how data integrates within an SAP architecture.

Strong stakeholder management experience at all levels

Experience of Business/IT partnering for the implementation of Data Governance-related solutions.

Experience with global working and across cultures.

Demonstrate good communication skills with the ability to influence others to achieve objectives

Ability to lead negotiations across a sophisticated group, to a target outcome.

Consistent record of delivery and ability to effectively prioritise to ensure goals and outcomes are achieved

Desirable for the role

S/4 HANA implementation programme experience.

Experience in life sciences and healthcare.

Experience in Data Governance

Experience in measuring, managing and improving Data Quality.

In-depth knowledge of relevant key business processes

Osirian Consulting is committed to working with our clients to promote equality and diversity in the workplace. We encourage and welcome applicants from all backgrounds and all sections of the community, and will never discriminate on the basis of race, gender, disability, or any other protected characteristic.

Please be aware that due to the high number of applications we receive, unfortunately we cannot respond to each application individually. If you do not hear back from one of our consultants within 14 days, then unfortunately you have not been shortlisted for this role.

Osirian Consulting is acting as a recruitment business in relation to this role

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.