Lead Mdm Engineer

Key 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 that master data is treated as a strategic asset.

Technical Lead Expertise Proven track record of managing complex data, MDM, or governance projects. Strong proficiency in AgileScrum 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

8 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 DB 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, ThoughtSpot, 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.