Data Governance Lead

Job Summary

Data governance at TDIndustries today is focused primarily on data quality within CRM and ERP, with heavy emphasis on Power BI reporting. As TDIndustries builds toward a mature, lakehouse-driven data capability, the Data Governance Lead is the role that changes that trajectory.

This is a practitioner role, not a policy writer. The right candidate starts by working directly alongside the business on active data quality challenges - particularly customer data standardization in Dynamics 365 - while concurrently building the foundational governance practice. Over time, this role matures governance across four core functions: metadata management, master data management, data quality engineering, and data contracts. This is a Senior Individual Contributor role requiring up to 15% travel, reporting directly to the VP, Data, Analytics & AI, and working in close partnership with the Sr. Solution Architect and Data Product Managers.

Essential Duties

Business Partnership & Data Quality

• Partner with business stakeholders across Construction, Facilities, and Building Services on active data quality issues - particularly in Dynamics 365 CRM/F&O - and drive structured, sustained remediation.
• Take ownership of ongoing customer data standardization efforts currently led by the business, bringing engineering rigor and tooling to accelerate progress.
• Define field-level business rules, valid value sets, and data standards for priority domains (customer 360, asset 360, Operations 360) and implement automated quality monitoring on the Snowflake platform.
• Build and maintain stewardship relationships with upstream data owners across Dynamics 365, Procore, Workday, and other operational systems.

Governance Framework & Practice Development

• Design and own the TDIndustries Data Governance Framework - data policy, data standards, quality monitoring standards, stewardship models, and supporting SOPs - built iteratively as the platform scales.
• Establish and maintain the enterprise business glossary and data dictionary as the single source of truth for entity definitions, system of record designations, and ownership boundaries.
• Stand up the Data Governance Council, define domain Data Owner and Data Steward roles, and operate governance cadences, reporting quality scorecards and adoption metrics to leadership.

Metadata Management

• Implement and operate the enterprise data catalog (Microsoft Purview preferred), enabling data discovery, lineage, and business-to-technical mapping across TD's data estate.
• • Enforce metadata standards ensuring every data asset carries documented ownership, classification, lineage, and business context; build lineage tracing from source systems through to analytics consumption.
  Master Data Management
• Establish MDM strategy and governance for critical domains - customer, site, project, asset, vendor, and workforce - defining system of record designations, golden record standards, and cross-system reconciliation rules.
• Serve as the decision authority for cross-system data conflicts; drive conformed dimension and reference data development to support consistent analytics across business units.
  Data Contracts
• Champion data contracts across the data platform - defining standards for schema, quality SLAs, and semantic commitments between data producers and consumers.
• Embed contract enforcement into data pipelines in partnership with Data Product Managers and engineering, preventing undeclared schema changes and quality degradation from reaching analytics and AI consumers.
  Collaboration & Team Development
• Collaborate with the Sr. Solution Architect on technical implementation of governance capabilities - ensuring governance is embedded in the platform, not layered on top of it.
• Partner with Data Product Managers to translate domain priorities into governed data product requirements and resolve cross-domain data conflicts.
• As the governance function matures, define team structure, mentor data quality engineers and stewards, and build playbooks that reduce key-person dependency.
• Continuously benchmark TD's governance maturity against DAMA-DMBOK, identifying gaps and sequencing a pragmatic roadmap that creates value at each stage.
• Perform other duties as required.

Minimum Requirements

• Bachelor's degree in information management, computer science, business, or related field preferred
• 8+ years of progressive experience in data governance, master data management, data quality, or data management. 
• Demonstrated experience building a governance program from an early stage - not inheriting an established framework, but designing and standing one up. 
• Proven ability to work directly with business stakeholders on data quality remediation, translating between business language and technical implementation. 
• Hands-on experience implementing metadata management, data lineage, or data catalog solutions in a cloud data platform environment. 
• Working knowledge of MDM patterns across enterprise multi-source-system environments. 
• Familiarity with DAMA-DMBOK; formal CDMP certification a plus. 
• Experience in construction, building services, or facilities management is a significant advantage - project-based business models, work order operations, and asset-centric data are directly relevant. 

Governance & Quality Tooling 

• Data catalog and metadata management - Microsoft Purview strongly preferred; Collibra, Alation, or equivalent considered. 
• Data quality automation - Great Expectations, Monte Carlo, Soda, dbt tests, or equivalent; experience building quality monitoring into cloud data pipelines. 
• MDM platforms or pattern-based MDM implementation on cloud data platforms. 
• Data contract frameworks - Data Contract Specification, Atlan, or custom enforcement via dbt/Snowflake. 

Data Platform (Working Proficiency) 

• Snowflake - access policies, data sharing, quality monitoring, and governance implementation; sufficient depth to partner credibly with data engineers. 
• Microsoft Fabric and Power BI - semantic model governance, sensitivity labels, information protection, and RBAC. 
• dbt - data quality tests, documentation, and lineage as a governance surface. 
• Azure Entra ID and RBAC - working knowledge of identity and access management as it applies to data platform governance.