Quantum - Data Conversion Lead

Cummins•Indianapolis, IN

2d•$120,000 - $180,000•Remote

About The Position

We are looking for a talented Quantum - Data Conversion Lead to join our team in Systems/Information Technology from your remote home office. In this role, you will make an impact in the following ways: Own the outcome 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. Plan & prioritize 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). Lead day‑to‑day execution 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. Coordinate across tracks 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. Design governance 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. Quality & controls 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. Issue management & decisioning 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. Cutover readiness 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. Stakeholder communication 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. People & partner leadership 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. Compliance & auditability 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). Continuous improvement Industrialize the data factory through automation and standardization. Track KPIs such as pass rates, reconciliation variances, and cycle times. Capture lessons learned and scale improvements globally.

Requirements

Data readiness for each release, country, and plant—clean, complete, and on time.
Data conversion strategy, standards, and guardrails.
End‑to‑end data conversion plan (objects, cycles, dependencies, milestones).
Conversion backlog prioritization with Build, Functional, and Test leads.
Alignment of data loads with environment refreshes and testing cycles (SIT, E2E, UAT, PAT).
Review of overnight data loads and conversion results; triage failures and data quality issues.
Daily work direction for data engineers and vendor SI partners to resolve root causes.
Accurate and repeatable mappings, transformation rules, and reconciliation steps.
Alignment of interfaces and conversions (structures, timing, cutovers) with Integration Leads.
Validation of business rules and required data shapes with Functional Leads.
Ensuring clean, stable data availability and resolution of data-tied defects with Testing teams.
Reviews of conversion designs, mappings, and exception handling.
Enforcement of global standards for reuse, automation, error handling, and auditability.
Approval of design changes impacting downstream processes, performance, or compliance.
Definition and enforcement of Definition of Ready / Definition of Done for conversion objects.
Monitoring of data quality thresholds (accuracy, completeness, duplicates, referential integrity).
Completion of reconciliation and business sign‑offs for each release.
Day‑to‑day trade‑off decisions (fix at source vs. transform vs. post‑load correction).
Escalation of issues only when schedule, cost, compliance, or scope is materially impacted.
Root‑cause analysis for recurring issues and institutionalization of permanent fixes.
Data conversion runbook, timing, staffing, and contingency plans.
Validation of mock conversions and cutover rehearsals meeting timing and quality targets.
Data activities during go‑live and support for rapid stabilization.
Clear, concise status updates—what’s on track, what’s at risk, and recommended actions.
Translation of technical data issues into business impact.
Communication of readiness, risks, and mitigation plans to program and leadership teams.
Coaching data engineers on scalable, reusable conversion patterns.
Management of vendor SI partners to outcomes and delivery commitments.
Building internal capability to reduce long‑term dependency on external partners.
Compliance of data conversions with security, privacy, and retention requirements.
Maintenance of end‑to‑end data lineage and traceability from source to SAP.
Up-to-date documentation (mappings, rules, reconciliation templates, approvals).
Industrialization of the data conversion factory through automation and standardization.
Tracking of KPIs such as pass rates, reconciliation variances, and cycle times.
Capture of lessons learned and scaling of improvements globally.

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.