Lead MDM Engineer

Responsibilities

• 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 processes.
• Team 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 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).
• Deep understanding of master data management principles, data governance frameworks, and large-scale data quality improvement initiatives.
• Proven ability to capture and document requirements, assess business impacts and trade-offs, and align stakeholders through interviews, workshops, and requirements harmonization.
• 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.
• Understanding of data architecture principles, data lineage, and experience maintaining data dictionaries/definitions.
• Exposure to governance and data quality tools such as Collibra or Data Hub.
• Ability to navigate a matrixed organization, work across time zones, drive consensus among competing priorities, and build strong stakeholder relationships.

• Strong problem‑solving skills with a track record of delivering results on time and within budget in large, complex programs.