Quantum - Data Conversion Lead

Cummins

1d•Remote

About The Position

The Quantum - Data Conversion Lead will join the Systems/Information Technology team, working from a remote home office. This role is critical for ensuring data readiness across all releases, countries, and plants, guaranteeing data is clean, complete, and delivered on time. Key responsibilities include defining data conversion strategies, standards, and guardrails, and overseeing their consistent adoption. The lead will manage the entire data conversion plan, prioritize the conversion backlog, and align data loads with various testing cycles. Daily execution involves reviewing data loads, triaging failures, resolving root causes with data engineers and vendor partners, and ensuring accuracy of mappings and transformation rules. The position requires extensive coordination with Integration, Functional, and Testing teams to validate business rules, data shapes, and resolve data-related defects. The lead will also design and enforce governance for conversion processes, define and monitor data quality thresholds, manage issues, and drive root-cause analysis. Furthermore, the role encompasses cutover readiness, stakeholder communication, leadership of people and partners, ensuring compliance and auditability, and driving continuous improvement through automation and standardization.

Responsibilities

Be the single owner for data readiness for each release, country, and plant—clean, complete, and on time.
Define the data conversion strategy, standards, and guardrails, and ensure consistent adoption.
Own the end‑to‑end data conversion plan (objects, cycles, dependencies, milestones).
Prioritize the conversion backlog with Build, Functional, and Test leads.
Align data loads with environment refreshes and testing cycles (SIT, E2E, UAT, PAT).
Review overnight data loads and conversion results; triage failures and data quality issues.
Direct daily work for data engineers and vendor SI partners to resolve root causes.
Ensure mappings, transformation rules, and reconciliation steps are accurate and repeatable.
Partner with Integration Leads to align interfaces and conversions (structures, timing, cutovers).
Collaborate with Functional Leads to validate business rules and required data shapes.
Work with Testing teams to ensure clean, stable data is available and defects tied to data are resolved.
Lead reviews of conversion designs, mappings, and exception handling.
Enforce global standards for reuse, automation, error handling, and auditability.
Approve design changes that impact downstream processes, performance, or compliance.
Define and enforce Definition of Ready / Definition of Done for conversion objects.
Set and monitor data quality thresholds (accuracy, completeness, duplicates, referential integrity).
Ensure reconciliation and business sign‑offs are completed for each release.
Make day‑to‑day trade‑off decisions (fix at source vs. transform vs. post‑load correction).
Escalate issues only when schedule, cost, compliance, or scope is materially impacted.
Drive root‑cause analysis for recurring issues and institutionalize permanent fixes.
Own the data conversion runbook, timing, staffing, and contingency plans.
Validate mock conversions and ensure cutover rehearsals meet timing and quality targets.
Lead data activities during go‑live and support rapid stabilization.
Provide clear, concise status updates—what’s on track, what’s at risk, and recommended actions.
Translate technical data issues into business impact.
Communicate readiness, risks, and mitigation plans to program and leadership teams.
Coach data engineers on scalable, reusable conversion patterns.
Manage vendor SI partners to outcomes and delivery commitments.
Build internal capability over time to reduce long‑term dependency on external partners.
Ensure data conversions comply with security, privacy, and retention requirements.
Maintain end‑to‑end data lineage and traceability from source to SAP.
Keep documentation current (mappings, rules, reconciliation templates, approvals).
Industrialize the data conversion factory through automation and standardization.
Track KPIs such as pass rates, reconciliation variances, and cycle times.
Capture lessons learned and scale improvements globally.