Overnight Data Correction Jobs (NOW HIRING)

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 endtoend 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 daytoday 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 signoffs are completed for each release.

Issue management & decisioning

• Make daytoday tradeoff decisions (fix at source vs. transform vs. postload correction).
• Escalate issues only when schedule, cost, compliance, or scope is materially impacted.
• Drive rootcause 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 golive 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 longterm dependency on external partners.

Compliance & auditability

• Ensure data conversions comply with security, privacy, and retention requirements.
• Maintain endtoend 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.

Cummins is an equal opportunity employer. Our policy is to provide equal employment opportunities to all qualified persons without regard to race, sex, color, disability, national origin, age, religion, union affiliation, sexual orientation, veteran status, citizenship, gender identity, or other status protected by law.

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

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 nontechnical 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, wellinformed decisions using available data, technical standards, and business context, while balancing risk, urgency, and longterm 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 endtoend 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 decisionmaking, supporting strong information governance across operational processes.
• Problem Solving - Solve complex problems using a disciplined, datadriven methodology by establishing problem traceability, identifying assignable and root causes, implementing robust solutions, and preventing recurrence while protecting the customer.