Data / AI Architect & Governance Analyst

Guidehouse•McLean, VA

About The Position

The Data / AI Architect & Governance Analyst evaluates enterprise data architecture, governance frameworks, integration patterns, ownership models, and data quality practices to identify gaps and improvement opportunities. The role assesses enterprise data ecosystems and recommends ways to improve interoperability, accessibility, reliability, and stewardship of organizational data assets. The position designs target-state data architectures that support analytics, reporting, artificial intelligence, machine learning, and emerging data-driven capabilities. It develops and enhances data governance frameworks, defines data domains and stewardship responsibilities, analyzes data flows and integration patterns, and supports data quality strategies and remediation. Working with business, technology, and leadership stakeholders, the role aligns data architecture and governance initiatives with organizational objectives. It also supports data modernization, digital transformation, and AI enablement through assessments, roadmaps, implementation planning, executive briefings, recommendations, and governance artifacts. The position may require up to 10% travel, and candidates must have the ability to obtain Public Trust clearance.

Requirements

Bachelor's degree in Information Systems, Data Management, Computer Science, Engineering, Business, or a related field.
At least 5 years of experience in data architecture, data governance, data management, analytics, or related disciplines.
Experience evaluating and designing enterprise data architectures, data governance programs, or data management frameworks.
Understanding of data governance principles, including data ownership, stewardship, metadata management, data quality, and master data management.
Experience assessing and documenting data flows, integration architectures, and business data requirements.
Knowledge of data warehousing, cloud-based data platforms, data integration technologies, and analytics environments.
Familiarity with AI, machine learning, advanced analytics, and the data requirements needed to support them.
Strong analytical and problem-solving skills, including the ability to translate complex technical concepts into business-focused recommendations.
Experience developing architecture diagrams, governance artifacts, strategic roadmaps, and executive presentations.
Excellent written and verbal communication skills and experience working with cross-functional stakeholders.
Ability to obtain Public Trust clearance.

Nice To Haves

Master's degree in Data Science, Information Management, Computer Science, Business Administration, or a related field.
Professional certifications in data management, governance, architecture, or cloud technologies, such as CDMP, DAMA, DCAM, TOGAF, AWS, Azure, or Databricks.
Experience establishing or maturing enterprise data governance organizations, councils, stewardship programs, or data operating models.
Experience supporting AI governance, responsible AI frameworks, model governance, or AI readiness assessments.
Knowledge of metadata management, data catalog, data lineage, master data management, and data quality platforms.
Experience supporting large-scale data modernization, cloud migration, or enterprise analytics transformation programs.
Experience with federal government, financial services, or highly regulated environments.
Familiarity with enterprise architecture frameworks and strategic data management best practices.
Experience developing data strategies, governance roadmaps, and implementation plans for executive leadership.
Knowledge of privacy, cybersecurity, records management, and regulatory requirements related to enterprise data management.

Responsibilities

Evaluate current-state data architecture, governance frameworks, integration patterns, ownership models, and data quality practices to identify gaps and improvement opportunities.
Assess enterprise data ecosystems and recommend strategies to improve interoperability, accessibility, reliability, and stewardship of organizational data assets.
Design target-state data architectures for analytics, reporting, AI, machine learning, and other data-driven capabilities.
Develop and enhance data governance policies, standards, processes, roles, and accountability models.
Define data domains, ownership structures, stewardship responsibilities, and governance operating models.
Analyze data flows, system interfaces, and integration patterns to improve data sharing and consistency across platforms.
Support development of data quality strategies, controls, metrics, and remediation approaches.
Collaborate with business, technology, and leadership stakeholders to align initiatives with organizational objectives.
Support data modernization, digital transformation, and AI enablement through architecture assessments, roadmap development, and implementation planning.
Develop executive briefings, recommendations, governance artifacts, and strategic roadmaps to guide data and AI investments.