Data Governance Lead LATAM

Data Governance Lead LATAM page is loaded## Data Governance Lead LATAMlocations: Mexico
- Mexico Citytime type: Full timeposted on: Posted Todayjob requisition id: R-148621

Job Description
Summary:As part of the LATAM Global Digital Network (GDN), this role will be responsible for designing, building and delivering Data Governance models and frameworks that strengthen Data Trust across role requires a strong combination of Data Governance expertise, framework development, technical understanding and structured problem-solving. The ideal candidate will be able to take complex data challenges and turn them into sustainable governance models with clear ownership, standards, processes, controls and measurable outcomes.
A key part of the role will be understanding the data landscape behind each governance challenge, including source systems, data platforms, transformations, integrations and consumption layers, and connecting that technical context with business needs to determine the right governance approach.
The successful candidate will combine strong knowledge of Data Management practices, ideally grounded in DAMA-DMBOK, with curiosity, proactivity and resourcefulness. This person should be comfortable navigating ambiguity, structuring complex initiatives, bringing the right stakeholders together and independently driving governance solutions from initial problem definition through adoption.
Ultimately, the role will help ensure that critical LATAM data is understood, owned, governed and trusted, creating stronger foundations for analytics, digital products, AI and agentic solutions. Beyond AI-ready data, the role will help develop governance frameworks for AI, machine learning and agentic use cases, addressing their distinct lifecycle, accountability and oversight requirements in partnership with the relevant AI, Technology, Security, Privacy and Legal teams.

Key Responsibilities
Data Governance & Data Trust* Design and deliver end-to-end Data Governance models for critical data domains, products and business capabilities across LATAM.
* Establish the governance mechanisms required to strengthen Data Trust, including ownership, stewardship, standards, controls, Data Quality, metadata and accountability.
* Develop scalable Data Governance frameworks, methodologies, standards and playbooks that can be consistently applied across different domains and markets.
* Define what \"trusted data\" means for different use cases and establish the governance requirements necessary to achieve and sustain it.
* Identify governance gaps and root causes behind recurring data issues and design sustainable solutions rather than isolated fixes.Data Management Frameworks* Apply Data Management principles across Data Quality, Master & Reference Data, Metadata, Business Glossary, Data Lineage, Data Classification and Data Ownership.
* Leverage DAMA-DMBOK principles and practices to bring structure and consistency to governance capabilities.
* Translate governance principles into practical operating models, decision rights, roles, processes and controls.
* Develop reusable governance assets that can scale across LATAM instead of creating isolated solutions for individual projects.Technical & Data Understanding* Understand the end-to-end lifecycle of data, from source systems through ingestion, transformation, storage and consumption.
* Work closely with Data Engineering, Architecture, Product and business teams to understand data flows, integrations, dependencies and platforms.
* Translate technical complexity into clear governance requirements and identify where governance controls should be embedded across the data lifecycle.
* Navigate modern data environments and technologies such as Microsoft Fabric, OneLake, Microsoft Purview, Azure, etc.AI, Machine Learning & Agentic Governance* Design and deliver governance frameworks for AI and agentic use cases in partnership with the relevant enterprise teams, distinguishing data readiness and Data Trust requirements from controls governing models, agents and their actions.
* Define practical lifecycle governance requirements, including use-case intake, risk assessment, accountable ownership, fit-for-purpose evaluation, approval criteria, monitoring and retirement.
* Establish Machine Learning Governance requirements across the model lifecycle, including training data documentation, model inventory and versioning, validation and approval, performance and drift monitoring, and criteria for retraining and retirement, with clear ownership and traceability.
* Establish governance requirements for agentic solutions, including boundaries of autonomy, access to data and tools, human oversight, escalation paths and traceability of decisions and actions.
* Translate enterprise Responsible AI principles into operational standards, controls and playbooks for LATAM, with clear responsibilities across Data Governance, AI, Technology, Security, Privacy and Legal teams.Initiative Leadership*