Job Summary

Role Designation: Data Management Analyst  

Overview  

The Data Management Analyst, Data & Insights - Data Governance serves as a bridge between the technical and business sides, translating complex data requirements into practical governance policies, standards, controls, and operating practices. This role helps ensure that data is trusted, well-defined, secure, and fit for business use by supporting data ownership, stewardship, data quality, metadata, lineage, and master data management practices. Working closely with stakeholders across business and technology teams, this role is responsible for implementing and continuously improving governance processes that increase the value, consistency, and responsible use of enterprise data.  

Key Responsibilities

Responsibilities for this position may include but are not limited to:  

• In addition to core data analyst capabilities such as data analysis, business communication, and problem solving, this role requires hands-on experience with enterprise data governance and data management practices. 

• Data Governance Operating Model: Support the implementation and continuous improvement of the data governance framework, including roles, responsibilities, decision rights, standards, policies, issue management, and governance routines. Coordinate with data owners, data stewards, and business stakeholders to embed governance practices into day-to-day operations. 

• Data Quality Management: Perform data profiling, assessment, monitoring, and remediation activities to improve data accuracy, completeness, consistency, timeliness, and fitness for use. Define data quality rules, thresholds, metrics, and reporting to track issues, root causes, and improvement progress. 

• Metadata, Business Glossary, and Lineage Management: Maintain business definitions, data element standards, ownership, critical data element documentation, and lineage information to improve shared understanding, traceability, and discoverability of data assets. Support data catalog and metadata repository adoption. 

• Master Data Management: Support the governance of core business data domains by helping define master and reference data standards, data ownership, matching and survivorship rules, quality controls, and change processes. Partner with business and technology teams to improve consistency of critical master data across systems. 

• Policy, Risk, and Compliance: Help ensure data practices align with internal policies, regulatory requirements, privacy obligations, and control expectations. Support audit readiness by maintaining documentation related to standards, lineage, access considerations, and governance decisions. 

• AI Governance: Support governance practices for AI agents and AI-enabled workflows by helping ensure appropriate data use, ownership, documentation, risk awareness, and alignment with responsible AI, privacy, security, legal, and enterprise data governance standards. Partner with business, data, and technology teams to promote trusted, well-governed, and compliant use of data in AI initiatives. 

• Data Standards and Architecture Collaboration: Partner with data architects, engineers, and platform teams to align governance requirements with data models, integration patterns, and solution designs. 

• Governance Reporting and Issue Management: Develop dashboards, scorecards, and management reports that track governance adoption, data quality performance, metadata coverage, stewardship engagement, and remediation status. Facilitate issue intake, prioritization, escalation, and follow-through. 

• Stakeholder Engagement, Stewardship, and Change Management: Act as a governance subject matter resource by providing guidance, training, and facilitation to data stewards and business partners. Lead working sessions that align stakeholders on definitions, controls, responsibilities, and remediation plans. 

• Continuous Improvement: Track industry practices, governance maturity, and emerging data management needs, including AI-ready data practices where relevant. Recommend and test process, control, and tool improvements that strengthen trust in enterprise data. 

Skill Requirements

Must-have: Data quality management, master data management, metadata management experience across various data types; experience with data catalog and data governance tools such as Collibra, Profisee, Databricks 

Required Qualifications  

• Proven experience in data governance, data management, data analysis or related roles within medium to large organizations. 

• Strong understanding of core data governance concepts, including data ownership and stewardship, policy and standards management, issue management, data controls, and governance operating models. 

• Hands-on experience with data quality management, including profiling, rule definition, monitoring, remediation, and metric development. 

• Hands-on experience with metadata management, business glossary development, data cataloging, and data lineage documentation. 

• Hands-on experience master data management concepts and practices, including master and reference data, domain ownership, data standards, matching and survivorship, and lifecycle controls. 

• Experience supporting AI governance practices that align with enterprise governance, privacy, security, and compliance requirements. 

• Proficiency in data analysis tools (e.g. SQL, Python) and reporting/BI platforms (e.g., Power BI). 

• Proficiency with data governance tools, data catalogs, metadata repositories, and related workflow platforms (e.g., Collibra, Profisee). 

• Solid understanding of data modeling, integration, and data architecture concepts relevant to governed data products and enterprise data assets. 

• Knowledge of relevant regulatory, privacy, risk, and control requirements affecting enterprise data. 

• Strong analytical, problem-solving, facilitation, documentation, and stakeholder engagement skills. 

• Ability to work independently and collaboratively to drive cross-functional governance initiatives and deliver high-quality outcomes. 

Other Requirements

Good-to-have: experience with AI governance and understanding of responsible AI and integrated governance between data and AI use cases 

Preferred Qualifications  

• Preferred Education: B.S. or M.S. in Information Systems, Computer Science, Management Information Systems, Business Analytics, Information Science, Engineering, Mathematics, or a related field. 

• Certified Data Management Professional (CDMP) 

• Data Governance and Stewardship Professional (DGSP) 

• Certified Information Systems Auditor (CISA) 

• Relevant vendor certifications in data governance, metadata, data quality, or master data management platforms (for example, Collibra or MDM-related tools). 

• Experience supporting enterprise data governance programs, master data management initiatives, data quality scorecards, or AI-ready data practices. 

• Familiarity with Oil and Gas industry data. 

• Ability to translate complex data topics into clear, practical guidance and compelling visual communication for business and technical audiences.