Quantum - Data Conversion Lead

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 conversion factory through automation and standardization.
• Track KPIs such as pass rates, reconciliation variances, and cycle times.
• Capture lessons learned and scale improvements globally.

To be successful in this role you will need the following: 

• Collaborates – Build strong partnerships across business, IT, and functional teams by proactively engaging stakeholders, aligning on shared goals, and contributing to outcomes that span organizational boundaries.
• Communicates Effectively – Deliver clear, tailored communications across multiple formats (verbal, written, visual) to ensure technical and non‑technical audiences understand decisions, risks, tradeoffs, and next steps.
• Customer Focus – Establish trusted relationships with internal and external customers by deeply understanding their needs and consistently delivering solutions that create measurable value and positive user outcomes.
• Decision Quality – Make timely, well‑informed decisions using available data, technical standards, and business context, while balancing risk, urgency, and long‑term impact.
• Manages Ambiguity – Move work forward effectively when requirements, data, or direction are unclear by asking the right questions, testing assumptions, and iterating toward clarity without stalling progress.
• Manages Complexity – Analyze and synthesize large volumes of complex or conflicting information to identify root issues, dependencies, and practical paths forward.
• Tech Savvy – Stay current on emerging technologies and digital tools, and proactively apply relevant innovations to improve solution quality, efficiency, and scalability.
• Quality Assurance Metrics – Apply IT Operating Model (ITOM) measurement practices, including SDLC standards, tools, metrics, and KPIs, to validate that solutions meet intended outcomes and quality expectations.
• Solution Design – Define end‑to‑end solution designs using industry standards, version control, and build/test automation, ensuring designs are buildable, measurable, secure, compliant, and aligned to business and technical requirements.
• System Solution Architecture – Create solution patterns aligned to the Cummins Technical Reference Model (CTRM), CLEAN standards, and approved reference architectures to ensure consistency, scalability, and governance compliance.
• Data Quality – Identify and correct data defects that impact reporting, analytics, and decision‑making, supporting strong information governance across operational processes.
• Problem Solving – Solve complex problems using a disciplined, data‑driven methodology by establishing problem traceability, identifying assignable and root causes, implementing robust solutions, and preventing recurrence while protecting the customer.

Education, Licenses, Certifications: 

• College, university, or equivalent degree in relevant technical discipline, or relevant equivalent experience required. 
• This position may require licensing for compliance with export controls or sanctions regulations. 

Experience: 

• Significant experience in a relevant discipline area is required. 
• Knowledge of the latest technologies and trends in data engineering are highly preferred and includes:
  - Analyzing complex business systems, industry requirements, and/or data regulations
  - A strong background in processing and managing large data sets
  - Proven Architecture and design experience in Big Data open source tools including but not limited to Java, Map-Reduce, SPARK, HBase, Hive, Kafka, ODBC and SQL query language
  - Proven design and development experience in clustered compute cloud-based implementation experience
  - Experience developing applications requiring large file movement for a Cloud-based environment and other data extraction tools and methods from a variety of sources
  - Experience in building analytical solutions
  - Experience in databases and data management
  Significant experiences in the following are preferred:
  - Experience with IoT technology 
  - Experience in Agile software development

Additional Responsibilities

• Define & Lead Data migration strategy: Full ownership of migration strategy including Mock cycles, SIT, UAT, and PROD cutover.
• Execute ETL cycles: Lead extraction, transformation, validation and load (maybe not because of the SI partner)
• Must drive cleansing, approvals, data readiness
• Critical partnership for data mapping & validation

Additional Competencies:

• Experience and thorough knowledge of SAP data structures
• Data profiling & validation tools such as BODS
• ETL concepts

Additional Experience:

• Delivery of multi-cycle programs
• SAP ERP data migration experience
• SAP business process knowledge