Sr Manager Data Management

Sr Manager, Data Management

We are seeking an experienced and passionate Sr Manager, Data Management to build and own all foundational enterprise data management capabilities, spanning Master Data Management (MDM), Data Governance, Data Quality, Metadata & Cataloging, Context Layer engineering, and Enterprise Data Architecture. This leader combines strategic oversight with deep hands-on expertise, ensuring that data across the organization is trusted, governed, discoverable, high-quality, and ready for analytics, AI, and operational use.

This role will also own semantic layer product roadmap, tooling selection/config, model patterns, release/change process, and adoption.

JOB DUTIES

Enterprise Data Management Leadership

• Own the strategy, roadmap, and execution of enterprise data management capabilities (MDM, governance, metadata/catalog, data quality, semantic/context layer, and enterprise data architecture standards).
• Build and run the data management operating model (data owners/stewards, decision rights, standards) and the governance cadence that drives adoption.
• Establish and operationalize data governance policies, standards, and stewardship models.
• Partner with business and technology leaders to ensure alignment, adoption, and measurable value.

Enterprise Master Data Management (MDM)

• Lead design, implementation, and ongoing operations of enterprise MDM solutions.
• Define and implement data models, match/merge rules, survivorship logic, hierarchies, and workflows.
• Ensure high-quality, authoritative master data for domains such as Customer, Product, Supplier, and Location.
• Deliver at least one priority master data domain to production and scale it with measurable improvements in accuracy, completeness, and timeliness.

Data Governance & Metadata Management

• Implement governance frameworks using platforms such as Collibra, Atlan, Informatica, Reltio, or equivalent.
• Build, launch and scale the data catalog and business glossary, including ownership, certification, and adoption across priority teams.
• Translate governance rules into technical controls and automated workflows.
• Partner with Security/Privacy to align data classification, access, and retention requirements with governance workflows

Data Quality & Scorecards

• Define enterprise data quality rules, thresholds, and monitoring processes.
• Build automated data quality pipelines and dashboards.
• Publish data quality scorecards and drive remediation with data owners and stewards.
• Establish a repeatable remediation cadence with data owners and stewards to resolve root causes and improve scorecards over time.

Context Layer & Semantic Modeling

• Own and operate the enterprise semantic layer in tooling (roadmap, patterns, releases, certification, adoption) and partner with Analytics Engineering on enablement and onboarding
• Govern metric definitions, canonical models, and shared data entities.

Enterprise Data Architecture

• Define and maintain enterprise data architecture standards, patterns, and reference models.
• Ensure alignment across data platforms, integration patterns, and cloud architecture.
• Provide architectural oversight for data ingestion, transformation, MDM, and consumption layers.

Hands-On Technical Execution

• Ensure reliable operations of data management platforms (MDM, catalog/governance, DQ), including configuration oversight, access, and release/change management.
• Design and implement critical workflows/integrations (APIs, lineage connectors, DQ rules) and establish reusable patterns the team can scale.
• Lead/resolve escalations/issues across data pipelines, metadata ingestion, and governance workflows as needed
• Act as a player/coach—personally dive in where needed while setting direction, coaching others, and unblocking delivery.

Team Leadership & Cross-Functional Collaboration

• Lead a team of data stewards, data quality analysts, and MDM/governance engineers.
• Drive enterprise adoption through training, communication, and change management.
• Serve as the primary point of accountability for all data management capabilities.

YOU MUST HAVE

• 7+ years in enterprise data management (MDM, governance, metadata/catalog, or data quality), including 3+ years leading teams and/or enterprise programs.
• Hands-on implementation experience with at least one MDM and/or data governance platform (e.g., Reltio, Informatica MDM/P360, Collibra, Atlan, or equivalent), including configuration, workflow design, and integration patterns.
• Strong technical foundation in data modeling, metadata/lineage concepts, data quality engineering, and semantic/metrics modeling.
• Experience building or operating an enterprise data catalog and business glossary, and partnering with stakeholders to drive adoption.
• Working knowledge of cloud data ecosystems (Azure, AWS, or GCP) and modern data platform patterns.
• Strong SQL skills and solid data engineering fundamentals (pipelines, integrations, testing/monitoring).
• Excellent communication, stakeholder management, and change leadership skills.

WE VALUE

• Experience with enterprise data architecture frameworks (TOGAF, DAMA-DMBOK).
• Background in operationalizing data governance programs at scale.
• Experience with API-based integrations, workflow automation, and metadata ingestion.
• Familiarity with analytics engineering tools (dbt, semantic layer tools, metric stores).

SUCCESS MEASURES

• Adoption: increased usage of certified/governed data assets, definitions, and metrics across priority teams.
• Quality: fewer recurring data quality incidents and demonstrable improvement in scorecards for critical data elements.
• MDM delivery: successful production implementation and scaling of priority master data domain(s) with clear ownership and remediation workflows.
• Discoverability: catalog/glossary coverage for priority datasets and improved lineage/metadata completeness.
• Operating rhythm: effective cross-functional governance cadence (owners/stewards/engineering/analytics) that resolves issues and drives standards adoption.