Customer 360 & MDM Data Specialist

Caterpillar•Nashville, TN

7d•Onsite

About The Position

The Customer 360 & MDM Data Specialist will play a key role within the Data & AI Solutions team, supporting the delivery of trusted, enterprise-wide customer data through Customer 360 capabilities and Master Data Management (MDM) practices. This hands-on position is responsible for customer identity resolution, match and merge processes, survivorship rules, customer ID management, data ingestion, stewardship operations, data quality, and practical data governance. The successful candidate will partner closely with business and technology stakeholders to resolve complex customer data challenges, support current-state operations, and identify opportunities to automate and modernize manual processes. With a strong foundation in customer master data, governance, and data quality, along with technical expertise in SQL, Snowflake, or similar data platforms, this individual will serve as a Customer 360 subject matter expert and help shape scalable, AI-enabled, and enterprise-ready customer data capabilities for the future.

Requirements

Master Data Management: Knowledge of master data management principles; ability to support customer identity resolution, match/merge, survivorship, hierarchy management, stewardship, and trusted customer data processes.
Data Quality Management: Ability to investigate, validate, monitor, and improve customer data quality through root cause analysis, business rule refinement, process improvement, and sustainable remediation.
Data Governance : Understanding of practical data governance concepts including ownership, stewardship, metadata, lineage, access, retention, auditability, and data quality controls.
SQL & Cloud Data Platforms: Ability to use SQL and modern data platforms such as Snowflake to analyze data, troubleshoot issues, validate outcomes, and support operational activities.
Technical Aptitude: Ability to understand and support modern data solutions, lightweight applications, APIs, workflow automation, and AI-enabled tools.
Stakeholder Communication: Ability to communicate complex customer data, governance, and technical concepts clearly to business, technical, compliance, and operational stakeholders.
Business Analysis: Ability to gather requirements, understand business needs, evaluate options, and translate problems into practical, scalable data solutions.
Continuous Improvement: Ability to challenge existing processes, identify automation opportunities, evaluate platform capabilities, and recommend better ways of working.
Bachelor’s degree with 5+ years of relevant experience, or Master’s degree with 3+ years of relevant experience.
Experience in Master Data Management, Customer Data Management, Customer 360, Data Quality, Data Governance, Data Stewardship, or related disciplines.
Strong understanding of customer MDM concepts such as match/merge, survivorship, customer identity resolution, golden records, customer hierarchies, and stewardship workflows.
Strong SQL skills and experience working with Snowflake or similar cloud data platforms.
Experience troubleshooting data ingestion, source-to-target mapping, data movement, data transformation, or operational data issues.
Experience supporting stewardship processes, exception management, data quality review, or business data ownership workflows.
Ability to work hands-on with data and technical teams while also contributing to process improvement and future-state planning.
Experience partnering with business, technology, compliance, risk, legal, or operational teams.
Familiarity with metadata, data catalog, lineage, data quality monitoring, workflow, or MDM platforms.
Strong analytical thinking, problem-solving, ownership, and ability to operate independently.
Curiosity and willingness to research, test, and recommend better approaches instead of relying only on existing processes.

Nice To Haves

Experience with Python, Streamlit, GitHub, Copilot Studio, Snowflake Cortex, or similar technologies is preferred.
Experience with MDM platforms such as Reltio, Profisee, Stibo, Informatica MDM, Snowflake Horizon, or similar tools is highly preferred.
Experience with Python, Streamlit, APIs, GitHub, Copilot Studio, Snowflake Cortex, or AI-enabled data solutions is preferred.

Responsibilities

Support Customer 360 processes, including customer identity resolution, match/merge, survivorship, customer ID assignment, and customer hierarchy-related activities.
Investigate customer data issues across source systems, ingestion processes, master data assets, and downstream applications.
Analyze duplicate records, unmatched records, matching logic, business rules, source system differences, and data quality exceptions.
Support the onboarding and integration of customer data sources into Customer 360 processes.
Research, document, and improve customer data rules, definitions, data flows, and operational procedures.
Facilitate, orchestrate, and monitor stewardship activities related to low-confidence customer matches, unmatched records, and customer data exceptions.
Monitor stewardship workload, aging, resolution timelines, and operational performance.
Partner with stewards and business stakeholders to improve exception handling, escalation processes, and stewardship workflows.
Perform root cause analysis on recurring data quality issues and recommend sustainable solutions.
Identify opportunities to reduce manual data cleanup through automation, process improvement, and better upstream controls.
Partner with Legal, Compliance, Risk, and business stakeholders to help ensure customer data usage, access, retention, and governance practices align with internal policies and applicable requirements.
Support the documentation and application of customer data definitions, business rules, ownership expectations, lineage, metadata, and quality controls.
Help translate data governance concepts into practical processes that improve data trust, usability, and accountability.
Support auditability and transparency of customer data assets, controls, and decision logic.
Use SQL, Snowflake, and related data tools to investigate customer data issues, validate data, and troubleshoot ingestion or transformation problems.
Support development and enhancement of data tools, dashboards, lightweight applications, workflows, and operational utilities.
Partner with technical teams or directly contribute to solutions using technologies such as Python, Streamlit, APIs, GitHub, Copilot Studio, Snowflake Cortex, or similar tools.
Evaluate opportunities to use existing platform capabilities before building custom solutions.
Contribute to AI-enabled capabilities such as customer data assistants, knowledge retrieval, workflow automation, and trusted data access.
Identify opportunities to improve Customer 360, MDM, stewardship, and data quality processes through automation and scalable operating models.
Help evaluate and mature the use of enterprise data catalog, metadata, workflow, data quality, and MDM platform capabilities.
Partner with application, API, and product teams to improve data quality closer to the source.
Support movement from manual, reactive cleanup toward proactive, automated, and business-owned data quality processes.
Contribute to the creation of trusted, AI-ready and analytics-ready customer data assets.