Senior Manager — SAP S/4HANA Data Conversion & Migration (Non-SAP to SAP)
Xylem is a Fortune 500 global water solutions company dedicated to advancing sustainable impact and empowering the people who make water work every day. As a leading water technology company with 23,000 employees operating in over 150 countries, Xylem is at the forefront of addressing the world's most critical water challenges. We invite passionate individuals to join our team, dedicated to exceeding customer expectations through innovative and sustainable solutions.
This role leads one of the highest-risk components of ERP transformation by ensuring accurate migration of legacy data into SAP S/4HANA. The position establishes migration strategies, data cleansing processes, validation controls, reconciliation procedures, and cutover readiness plans. High-quality migration directly impacts business continuity, user adoption, and post-go-live operational stability. The role protects the digital core by ensuring that critical business data is complete, accurate, and fit for future operations. Successful data migration is a foundational requirement for realizing value from the ERP transformation investment.
Key Responsibilities
Conversion strategy and execution
● Own the end-to-end data conversion strategy: scope, object inventory, source-system profiling, extraction
approach, transformation rules, load sequencing, and validation design.
● Lead legacy data discovery across non-SAP ERPs where documentation is thin and business logic is
undocumented, reverse-engineering meaning from the data itself rather than relying on legacy system owners
alone.
● Design and operate the migration factory — reusable templates, mappings, rule libraries, and automation —
so each wave costs less and runs cleaner than the last.
● Own mock conversion cycles end to end, with defined defect thresholds and reconciliation criteria as formal
gates into cutover.
● Plan and execute production cutover loads, including timing, dependencies, fallback, and post-load
reconciliation.
Data quality and readiness
● Establish measurable data quality standards with business-owned acceptance criteria per object, and report
readiness as a number, n...
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