MDM Engineer

• Strategy & Governance: Define and lead the MDM strategy including data governance policies stewardship and the creation of a golden record for domains like Customer Supplier or Product.
• Implementation & Architecture: Design and implement MDM technology solutions including data modeling data integration match/merge rules and ETL Leadership: Lead mentor and manage a team of MDM professionals (data analysts stewards) setting performance expectations and managing project deliverables.
• Cross-Functional Collaboration: Partner with business leaders (Sales Finance Marketing) and IT to translate business requirements into technical design specifications.
• Data Quality & Monitoring: Oversee the establishment of data quality rules monitoring KPIs and resolving data quality issues.
• Change Management: Drive organizational change to adopt new data standards ensuring that master data is treated as a strategic asset.
• Proven track record of managing complex data MDM or governance projects.
• Strong proficiency in Agile/Scrum methodologies and tools (e.g. Jira Confluence Smartsheet MS Project).
• Master Data & Governance: Deep understanding of master data management principles data governance frameworks and large-scale data quality improvement initiatives.
• Proven ability to capture/document requirements assess business impacts and tradeoffs and align stakeholders through interviews workshops and requirements harmonization.

Technical Data Skills:

• 7 years of experience in data analysis specifically focused on data-related initiatives MDM or data governance.
• Industry-standard certifications in data management or analytics preferred.
• Deep familiarity with D&B data architecture hierarchies and enrichment preferred.
• Hands-on experience with data profiling source system analysis and KPI definition.
• Strong SQL & Python expertise and experience querying large complex data sets.
• Experience with at least one analytics platform (e.g. Hadoop Spark Snowflake).
• Experience with BI tools such as Tableau Thought Spot or Business Objects.
• Data Architecture & Quality: Understanding of data architecture principles data lineage and experience maintaining data dictionaries/definitions.
• Tools & Platforms: Exposure to tools such as Collibra Data Hub (or equivalent for governance and data quality).
• Collaboration & Communication: Ability to navigate a matrixed organization work across time zones drive consensus among competing priorities and build strong stakeholder relationships.

• Execution & Accountability: Strong problem-solving skills with a track record of delivering results on time and within budget in large complex programs