Director, Data Management and AI Readiness

Deloitte Global is the engine of the Deloitte network. Our professionals reach across disciplines and borders to develop and lead global initiatives. We deliver strategic programs and services that unite our organization.

Work you'll do

Deloitte Global is the engine of the Deloitte network. Our professionals reach across disciplines and borders to develop and lead global initiatives. We deliver strategic programs and services that unite our organization.

The Director, Data Management & AI Readiness is responsible for defining and leading the strategy, operating model, and delivery of enterprise data management capabilities.  The role is the business owner for cross-domain data management capabilities that serve the Deloitte network, including Master Data Management (MDM), Metadata Management, Data Quality, Taxonomy Management, Reference Data, and Data Modeling.

Working across global applications, business functions, architecture teams, product organizations, and operational teams, the Director will establish scalable data management processes, drive adoption of enterprise standards and procedures, and develop trusted data assets for business operations, analytics, AI, regulatory compliance, and decision-making.

This leader serves as a key connector between business executives, application owners, enterprise architects, operations leaders, and technology teams, aligning diverse stakeholder perspectives and driving consensus on complex data management decisions across a global organization. This role is expected to establish measurable outcomes, monitor performance, and continuously evolve enterprise data management capabilities to an aligned target state and maturity model that the role will also define and govern.

The DSGO is responsible for building an integrated strategy and operating model to manage global data and AI products across their lifecycle, enabling better business outcomes, innovation, and client service.

Key responsibilities

1. Strategy & Leadership

• Define and execute the data management strategy for:
  • Master Data Management
  • Metadata Management
  • Ontology and Context Management
  • Data Quality
  • Taxonomy and Reference Data
  • Enterprise Data Models
• Develop multi-year roadmaps that align to business priorities, digital transformation objectives, and AI enablement goals.
• Establish and evolve enterprise operating models, governance forums, stewardship structures, and accountability frameworks.
• Champion the importance of trusted, governed data as a strategic enterprise asset.
• Lead a global team of data management professionals and subject matter experts.

2. Data Management Operating Model Development

• Design and implement sustainable operating models supporting global and regional data domains.
• Define roles, responsibilities, decision rights, escalation paths, and governance forums.
• Build effective collaboration models between:
  • Business teams
  • Application owners
  • Product teams
  • Enterprise Architecture
  • Operations teams
  • Data and analytics functions
• Establish service delivery models that enable consistent execution across multiple geographies and applications.
• Drive adoption of standardized data management processes for, metadata, data quality, data architecture, taxonomy, and data quality remediation.

3. Master Data & Reference Data Management

• Lead enterprise master data programs across key domains such as Customer, Vendor, Products, and People.
• Define authoritative sources, golden record strategies, hierarchy management approaches, and lifecycle management processes.
• Establish data management practices for reference data and shared enterprise dimensions.
• Partner with business and technology leaders to modernize master data capabilities and improve operational efficiency.

4. Metadata, Taxonomy & Data Modeling

• Establish enterprise metadata management capabilities including technical, operational, and AI metadata.
• Enable the integration of business glossaries and semantic frameworks across applications and data products.
• Lead development of conceptual, logical, and physical data modeling standards.
• Ensure enterprise data structures support interoperability, analytics, AI, and future platform modernization initiatives.

5. Data Quality & Measurement

• Lead enterprise data quality management capabilities including strategy, frameworks, procedures, monitoring, and remediation
• Operationalize quality dimensions, monitoring capabilities, issue management processes, and continuous improvement practices.
• Partner with business and technology teams to improve the quality, consistency, integrity and usability of enterprise data across critical business domains.
• Define and implement enterprise measurement frameworks, KPIs, and scorecards to assess the effectiveness and maturity of data management capabilities, including data issue resolution and remediation
• Build executive dashboards and performance reporting that provide visibility into enterprise data health, operational performance, risks, and business outcomes.
• Drive continuous improvement programs using quantitative performance measures, automation and operational metrics to increase the effectiveness of enterprise data management.

6. Stakeholder Management & Executive Influence

• Build trusted relationships with senior executives, business leaders, product leaders, architects, and operations leaders.
• Facilitate decision-making across highly complex environments with competing priorities and differing viewpoints.
• Lead discussions where stakeholder opinions may be polarized and drive alignment toward enterprise outcomes.
• Negotiate priorities, funding, resource commitments, and roadmap decisions across organizational boundaries.
• Influence and build consensus without authority in a highly matrixed global environment.
• Present program strategy, risks, decisions, and results to executive and governance forums.

7. Innovation & Capability Development

• Identify new capabilities required to support enterprise data and AI strategies.
• Evaluate emerging technologies, tools, and operating practices.
• Lead modernization initiatives related to:
  • Data governance
  • Metadata platforms
  • Data quality tooling
  • Master data platforms
  • AI-ready data foundations
  • AI-auditability for data assets
• Build communities of practice and increase organizational data management maturity

The team

Deloitte Technology works at the forefront of technology development and processes to support and protect Deloitte around the world. In this truly global environment, we operate not in "what is" but rather "what can be" to help Deloitte deliver and connect with its clients, its communities, and one another in ways not previously conceived.

Qualifications

Key Qualifications:

• Bachelor's degree in Information Management, Computer Science, Data Management, Business, or related discipline.  Master's degree preferred.
• 12+ years of experience in enterprise data management, data governance, data architecture, or related disciplines.
• 7+ years leading global teams and enterprise-scale transformation initiatives.
• Proven leadership of master data, metadata, data quality, taxonomy, or data governance programs as the voice of the business with technical teams, particularly for cross-domain data assets.  Experience with data governance tools such as Informatica, SAP MDG, Collibra, Databricks Unity Catalog, etc.
• Experience establishing operating models and governance frameworks across multiple business units and geographies. Experience working closely with application and operations teams in large, federated organizations.
• Confident communicator with the ability to engage effectively with executive leadership teams.  Able to translate complex data concepts into business outcomes.
• Ability to build strong relationships across organizational boundaries, acting as a trusted advisor to executives, business leaders, architects, product teams, and internal customers represented across multiple geographies.
• Demonstrated experience navigating politically complex environments and skilled at facilitating alignment where competing objectives exist, to successfully balance enterprise objectives with individual stakeholder requirements.
• Connects enterprise data capabilities to business strategy, operational effectiveness, and AI enablement by establishing measurable outcomes and accountability structures.
• Drives execution through governance, influence, and disciplined delivery management leading direct reports and dotted line teams.