Consultant - Data Management

Technical

• Data governance and data management frameworks .Data ownership and stewardship models
• Business glossary and metadata management. Data quality assessment and profiling
• Data quality rule and control definition. SQL and data querying
• Data validation and reconciliation.Exception and root cause analysis
• Data classification and lifecycle management. Data lineage and impact analysis
• Data standards and business rules. Data catalog and governance platforms
• Data quality and profiling tools.Data issue and remediation management
• Governance reporting and data quality scorecards
• Business and data requirements analysis
• Data management documentation and deliverable development

Behavioral

• Analytical and structured thinker. Strong attention to detail
• Evidence-driven and quality-conscious
• Curious and investigative.Clear and confident communicator
• Strong stakeholder engagement skills
• Able to facilitate discussions across business and technology teams
• Comfortable working with incomplete, inconsistent, or evolving information
• Able to organize complex information into practical and understandable outputs
• Proactive in identifying gaps and following issues through to resolution
• Able to manage multiple activities and priorities
• Collaborative while capable of working independently. Strong documentation discipline
• Client-focused and professional

Responsibilities

Operational

Support the design and implementation of data management frameworks, operating models, policies, standards, procedures, and controls aligned with client requirements and applicable organizational or regulatory expectations.
Assess existing data management practices and identify gaps related to data ownership, stewardship, definitions, metadata, data quality, standards, controls, processes, and governance adoption.
Define and maintain data governance structures, including data domains, data owners, data stewards, responsibilities, governance forums, escalation paths, and supporting governance documentation.
Develop and maintain business glossaries, data definitions, metadata, data dictionaries, and related governance information in collaboration with business and technical stakeholders.
Perform data profiling and data quality analysis to identify completeness, validity, consistency, uniqueness, integrity, accuracy, timeliness, conformity, and other relevant data issues.
Translate business expectations into measurable data rules and controls, including data quality rules, validation logic, thresholds, acceptance criteria, and monitoring requirements.
Investigate data issues and exceptions using data evidence, business rules, process knowledge, source-system behavior, transformation logic, and stakeholder input.
Document and manage data issues through their lifecycle, including issue description, evidence, affected data, ownership, business impact, root cause information, corrective actions, status, and closure evidence.
Work with data owners, stewards, business teams, and technology teams to define practical remediation actions and follow identified issues through validation and closure.
Support the implementation and use of data governance, catalog, metadata, and data quality platforms, including configuration requirements, metadata population, rule implementation, workflow requirements, testing, and user adoption.
Support data classification, lineage, lifecycle, reference data, and master data activities where required as part of broader data management engagements.
Facilitate stakeholder interviews, workshops, working groups, and validation sessions to gather requirements, resolve data management questions, confirm ownership, validate definitions, and agree data controls.
Prepare data management deliverables and client-facing materials, including assessment reports, governance documentation, data quality findings, issue registers, dashboards, scorecards, presentations, decision logs, and implementation plans.
Monitor data governance and data quality activities through defined metrics and reporting, helping stakeholders understand progress, open gaps, quality trends, unresolved issues, and required actions.
Support integration of data management requirements into data migration, analytics, reporting, data platform, AI, and digital transformation initiatives so that governance and quality requirements are considered throughout delivery rather than addressed separately.
Review data management outputs for accuracy, consistency, completeness, and usability before submission to clients or project leadership.
Coordinate effectively across business, data, technology, risk, compliance, privacy, and project teams to ensure data management requirements are