SAP Data Management and Migration Senior Manager

KPMG
Manchester
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Business Analyst Data Analytics & AI Remote

Senior Business Analyst Data Analytics & AI Remote

Portfolio Revenue & Debt Data Scientist

Integration & Data Architect

Portfolio Revenue & Debt Data Scientist

Senior SAP MDG Expert — Product Master Data Architect

Job description

SAP Data Management and Migration Senior Manager

 

We are seeking talented individuals with deep design and implementation experience in end to end SAP Data management and Data migration solutions. This is a high-profile role within the team, requiring strong client delivery skills, whilst supporting business development opportunities to grow the practice.

 

The successful candidate will be:

 

Experienced in implementation of large-scale data management and migration solutions within a SAP S4 HANA transformation program Experienced in implementing SAP Master data management for core data objects such as Material, Business Partner (Customer, Vendor, Employee) and Finance. Responsible for data architecture, data design, managing and maintaining a repeatable data load and migration process using tools like LSMW, LTMC/Migration cockpit etc. Able to advise clients in designing solutions using SAP Datasphere and its integration to SAC and other BI tools Perform data analysis related to Process intelligence/mining activities for clients using SAP Signavio Experienced in SAP data governance design, ownership, and management. Capable of supporting business development and sales initiatives including bid and proposal support in the SAP Data management area

 

 

The Person

 

Demonstrable experience of having successfully delivered end-to-end SAP S/4 HANA Data Management and Migrations Transformation programmes within a Consulting environment SAP S/4 HANA system design, build and deployment experience – 3 full lifecycle implementations preferred Demonstrable experience in running and supporting pre-sales activities- RFPs, demos, client engagement Strong documentation, reporting and presentation skills Well-developed analytical skills and the ability to provide clarity to complex issues, and synthesize large amounts of information Strong interpersonal, team building, organisational and motivational skills Strong knowledge of S/4HANA configuration and best practices Experience producing project deliverables (business requirements, functional specs, configuration document, process flows, use cases, requirements traceability matrices etc.) Detailed working knowledge of how processes are enabled within SAP Experience of facilitating a design workshop and then translating the requirements into design Ability to build strong client relationships based on subject matter expertise and quality of delivery Proven ability to collaborate and build strong relationships with varying team members

#LI-AP1

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.

Education Jobs

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

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.

Jobs

Neurodiversity in Data Science Careers: Turning Different Thinking into a Superpower

Data science is all about turning messy, real-world information into decisions, products & insights. It sits at the crossroads of maths, coding, business & communication – which means it needs people who see patterns, ask unusual questions & challenge assumptions. That makes data science a natural fit for many neurodivergent people, including those with ADHD, autism & dyslexia. If you’re neurodivergent & thinking about a data science career, you might have heard comments like “you’re too distracted for complex analysis”, “too literal for stakeholder work” or “too disorganised for large projects”. In reality, the same traits that can make traditional environments difficult often line up beautifully with data science work. This guide is written for data science job seekers in the UK. We’ll explore: What neurodiversity means in a data science context How ADHD, autism & dyslexia strengths map to common data science roles Practical workplace adjustments you can request under UK law How to talk about your neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in data science – & how to turn “different thinking” into a real career advantage.

Jobs

Data Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

🍪 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.