Lead end-to-end SAP data migration for large S/4HANA programs: analyze legacy and target models, define mappings, perform profiling and cleansing, build migration datasets with SAP tools, develop reusable frameworks and automation, create data quality dashboards, support cutover and post-go-live stabilization, and mentor data teams.
We are looking for a talented P2B - Data Analyst Principal to join our team in Systems/Information Technology for Cummins Darlington, County Durham, United Kingdom.
In this role, you will make an impact in the following ways:
- Lead end-to-end SAP data migration activities across large-scale SAP S/4HANA transformation programs.
- Analyze legacy data sources and SAP data models to define and execute source-to-target mappings for master and transactional data.
- Perform data profiling, quality assessments, root cause analysis, and data validation to ensure completeness, consistency, and accuracy.
- Drive data cleansing, enrichment, standardization, remediation, and reconciliation activities across multiple business functions.
- Design, build, validate, and execute migration datasets using SAP migration tools including SAP BODS, BDC, and other approved migration technologies.
- Develop reusable migration frameworks, automation capabilities, data quality controls, governance standards, and best practices to support scalable delivery.
- Apply advanced analytical techniques to identify data quality, integrity, redundancy, and integration issues, recommending sustainable solutions across the data lifecycle.
- Create dashboards, reports, metrics, and visualizations that provide actionable insights into data quality, migration readiness, risks, and program health.
- Establish and monitor enterprise data quality metrics and support ongoing data governance initiatives and compliance requirements.
- Support ETL development, transformation, load execution, mock conversions, dress rehearsals, cutover planning, production deployment, and post-go-live stabilization.
- Collaborate closely with business stakeholders, SAP functional consultants, solution architects, technical teams, and program leadership to ensure successful migration outcomes.
- Provide technical leadership, coaching, and mentoring to data analysts and project teams while promoting best practices in data management and governance.
- Stay current with emerging technologies including AI-assisted migration capabilities, SAP Business AI, SAP Joule, cloud data platforms, and modern data engineering practices.
To be successful in this role you will need the following:
- 10+ years of experience in data management, data analysis, data quality, or SAP data migration.
- Proven experience delivering SAP data migration workstreams within large-scale SAP S/4HANA transformation programs.
- Strong understanding of SAP S/4HANA business processes, data structures, and enterprise data models.
- Hands-on experience migrating both master and transactional data across domains such as Plan to Build (P2B), Material Master (MM), and Extended Warehouse Management (EWM).
- Strong expertise in data extraction, transformation, cleansing, reconciliation, validation, and migration execution.
- Experience with SAP BODS, BDC, ETL tools, SQL, NoSQL databases, and business intelligence platforms.
- Strong knowledge of data governance, data quality frameworks, profiling techniques, metadata management, and data catalog solutions.
- Experience developing migration rules, reusable frameworks, automation solutions, and governance standards.
- Ability to independently manage complex technical challenges while influencing cross-functional stakeholders.
- Strong communication, analytical, problem-solving, and project management skills.
Preferred Experience:
- Experience with SAP S/4HANA 2021 or later implementations and upgrade programs.
- Knowledge of SAP Business AI capabilities and SAP Joule.
- Experience leveraging AI-assisted tools for data profiling, mapping, migration acceleration, and quality assessment.
- Experience with Agile software delivery methodologies.
- Exposure to cloud-based data platforms, clustered computing, IoT technologies, and Big Data ecosystems.
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