Customer 360 & MDM Data Specialist

Caterpillar Inc.•Nashville, TN

2d•$94,728 - $151,572•Onsite

About The Position

The Governance, Data Quality & AI Readiness Lead will play a critical role within the Data & AI Solutions team, establishing and operationalizing trusted, governed, and high-quality enterprise data that enables Customer 360, Master Data Management (MDM), data products, analytics, regulatory compliance, and emerging AI capabilities. This position serves as a bridge between business stakeholders, technology teams, Compliance, Legal, Risk, and Enterprise Governance functions to ensure enterprise data is governed, trusted, and ready to support strategic business and AI-enabled outcomes. The successful candidate will combine strategic thinking with hands-on execution, helping mature governance and data quality capabilities while simplifying, automating, and scaling processes through modern technologies and operating models.

Requirements

Bachelor's degree in Information Systems, Computer Science, Data Analytics, Business, Engineering, or related discipline.
5+ years of experience in Data Governance, Data Quality, Data Management, MDM, Business Intelligence, Analytics, or Information Management disciplines.
Experience working with business stakeholders and technical teams to solve complex data challenges.
Experience documenting business requirements, policies, standards, business rules, controls, and governance processes.
Experience with SQL and data analysis techniques.
Strong communication, facilitation, and stakeholder management skills.

Nice To Haves

Experience with Data Governance, Data Quality, Metadata Management, Data Stewardship, MDM, or Information Management programs.
Experience supporting Customer 360, Golden Record, or customer master data initiatives.
Experience working within Financial Services or other regulated industries.
Knowledge of GDPR, CCPA, GLBA, privacy, compliance, records management, and risk management practices.
Experience supporting enterprise data strategy, data products, analytics, AI, or digital transformation initiatives.
Understanding of Responsible AI, AI Governance, and AI Readiness concepts.

Responsibilities

Operationalize enterprise data governance standards, policies, ownership models, and stewardship processes.
Facilitate data ownership, stewardship, accountability, and governance activities across business domains.
Partner with Compliance, Legal, Risk, Privacy, and Information Security teams to implement governance controls and privacy requirements.
Support compliance with applicable frameworks including GDPR, CCPA, GLBA, records management, data retention, and internal governance standards.
Drive data classification, sensitive data management, retention, consent, and authorized data usage practices.
Support governance councils, issue management, audit readiness, and governance maturity initiatives.
Define, implement, and monitor data quality controls, scorecards, KPIs, and critical data elements.
Investigate data quality issues, perform root-cause analysis, and coordinate sustainable remediation.
Establish quality rules, thresholds, monitoring processes, and exception management workflows.
Expand automated monitoring and data observability capabilities to proactively identify issues.
Promote shift-left quality practices that improve data quality at the source.
Partner with Customer 360 and MDM teams to support trusted customer data management practices.
Define governance and quality requirements supporting identity resolution, match/merge, survivorship, and Golden Record processes.
Improve consistency and interoperability across customer, dealer, and enterprise data domains.
Establish governance controls supporting trusted master data assets and Customer 360 initiatives.
Expand metadata management, data lineage, business glossary, and data catalog capabilities.
Improve discoverability, transparency, ownership, and usability of enterprise data assets.
Develop and maintain lineage documentation, critical data element inventories, business glossaries, and governance artifacts.
Partner with Architecture and Engineering teams to embed governance controls into data platforms and delivery pipelines.
Identify opportunities to simplify, automate, and scale governance, stewardship, and data quality processes.
Implement workflow automation that reduces manual effort and improves governance efficiency.
Support governance workflows including approvals, issue management, access reviews, and policy administration.
Leverage Power Platform, Snowflake, APIs, AI-enabled tools, and workflow technologies to modernize operations.
Establish trusted, governed data assets that support AI and advanced analytics initiatives.
Support AI readiness assessments and AI governance programs across priority business domains.
Partner with Data Science, Engineering, and AI teams to improve lineage, provenance, data quality, and traceability.
Help define controls supporting AI model input quality, explainability, auditability, and responsible AI practices.
Ensure enterprise data can be leveraged safely and effectively for future AI-enabled solutions.