"12–16 years overall, including 7+ years in Data Governance/Data Management and 3+ years in a lead or architecture role
Must Have Technical/Functional Skills
- 12+ years of experience in enterprise data management, with strong expertise across Data Governance, Data Quality, Metadata, Data Lineage and Data Observability.
- Proven experience defining and implementing enterprise Data Governance operating models, policies, standards, controls, stewardship structures, decision rights and governance forums.
- Strong hands-on knowledge of business and technical metadata, data catalog, glossary, classification, ownership, critical data elements and end-to-end lineage.
- Deep understanding of Data Quality frameworks, including profiling, rule design, controls, scorecards, issue management, root-cause analysis, remediation and continuous monitoring.
- Experience establishing Data Observability capabilities spanning freshness, volume, schema, distribution, pipeline health, anomalies, incidents, service levels and operational dashboards.
- Ability to integrate governance, quality, lineage and observability controls across cloud, lakehouse, data warehouse, data integration and analytics ecosystems.
- Experience with one or more enterprise platforms such as Collibra, Informatica CDGC/Axon/EDC/IDQ, Microsoft Purview, Alation, Atlan, BigID, Monte Carlo, Bigeye, Databricks Unity Catalog or comparable tools.
- Strong knowledge of data architecture, data modeling, ETL/ELT, APIs, batch/streaming pipelines, SQL and cloud data platforms such as Azure, AWS, Databricks or Snowflake.
- Experience defining governance and quality KPIs/KRIs, control evidence, data issue workflows, auditability and executive reporting.
- Good understanding of privacy, security, retention, regulatory and risk-management expectations relevant to financial services, including BCBS 239, GDPR/CCPA and data-control principles.
- Proven experience leading discovery workshops, current-state assessments, target-state design, roadmaps, tool evaluations, implementation planning and adoption/change initiatives.
- Strong consulting, stakeholder-management and communication skills, with the ability to engage business, risk, compliance, data, engineering and executive stakeholders.
- Preferred certifications in DAMA/CDMP, DCAM, Collibra, Informatica, Microsoft Purview, cloud data platforms, data quality or related disciplines.
Roles & Responsibilities
- Lead the design and implementation of an integrated Data Governance, Data Quality, Data Lineage and Data Observability capability for enterprise data platforms.
- Translate business, regulatory, risk and technology needs into pragmatic governance policies, standards, controls, processes and implementation roadmaps.
- Define the operating model, including accountable data owners, business and technical stewards, governance councils, RACI, escalation paths and decision rights.
- Establish enterprise metadata, business glossary, data catalog, classification, critical data element and ownership practices.
- Design Data Quality lifecycle processes covering profiling, rule definition, thresholds, monitoring, scorecards, issue triage, root-cause analysis and remediation governance.
- Define and operationalize end-to-end lineage from source through transformations to reports, analytics, regulatory outputs and AI use cases.
- Establish observability monitoring for data freshness, completeness, volume, schema, distribution, pipeline reliability, anomalies and data incidents; align alerts with support and remediation workflows.
- Develop control frameworks, KPIs/KRIs, dashboards and executive reporting to demonstrate data health, control effectiveness, risk reduction and adoption.
- Evaluate and configure governance tooling; define integration patterns with data platforms, ETL/ELT, BI, ticketing, CI/CD and cloud services.
- Partner with data architecture, engineering, platform, security, privacy, risk and business teams to embed governance controls within delivery lifecycle and DataOps practices.
- Lead workshops, architecture/design reviews, backlog prioritization, implementation governance, testing, release readiness and production adoption.
- Manage risks, dependencies and issues; provide clear recommendations and escalate decisions requiring senior stakeholder intervention.
- Mentor governance leads, data stewards, analysts and engineers; create reusable accelerators, templates, playbooks and knowledge assets.
- Support solutioning, proposals, effort estimation, client presentations and capability development for Data Governance and Data Management opportunities.
Generic Managerial Skills, If any
- Strong leadership and stakeholder-management skills across business, technology, risk, compliance and data organizations.
- Excellent written, verbal and presentation skills for technical, business and executive audiences.
- Consultative problem-solving approach with the ability to simplify complex governance and technology topics.
- Experience leading cross-functional and geographically distributed teams and driving outcomes without direct authority.
- Strong planning, prioritization, facilitation, negotiation, risk management and decision-making skills.
- High ownership, accountability and attention to detail, with a focus on measurable adoption & sustainable operating practices.
- Ability to mentor practitioners, influence senior stakeholders and establish an active data stewardship community.
Key Words to search in Resume
Data Governance, Data Quality, Data Lineage, Data Observability, Metadata Management, Data Catalog, Business Glossary, Critical Data Elements, Data Ownership, Data Stewardship, Governance Operating Model, Data Policies, Data Standards, Data Controls, DQ Rules, Data Profiling, DQ Scorecards, Data Issue Management, Root Cause Analysis, Data Health, Data Reliability, Freshness, Volume, Schema Drift, Distribution, Anomaly Detection, Pipeline Monitoring, Data SLAs, Data SLOs, Data Incidents, Collibra, Informatica CDGC, Axon, EDC, IDQ, Microsoft Purview, Alation, Atlan, BigID, Monte Carlo, Bigeye, Unity Catalog, Databricks, Snowflake, Azure, AWS, SQL, ETL, ELT, DataOps, Cloud Data Platform, BCBS 239, GDPR, CCPA, DCAM, DAMA, CDMP, BFSI, Financial Services
Pre-Screening Questionnaire
- Describe an enterprise Data Governance program you led. What was the scope, your role, the operating model and the measurable outcome?
- How have you integrated Data Governance, Data Quality, Lineage and Observability rather than implementing them as separate capabilities?
- Explain how you define data ownership, stewardship, critical data elements, policies, standards, controls, RACI and governance forums.
- Describe a Data Quality framework you implemented. How were rules, thresholds, scorecards, issues, root causes and remediation managed?
- How have you captured and validated end-to-end lineage across source systems, transformations, data platforms and business or regulatory reports?
- What Data Observability use cases have you implemented for freshness, volume, schema, distribution, pipeline health and anomaly detection? How were alerts operationalized?
- Which governance, catalog, DQ, lineage and observability tools have you configured or implemented hands-on? Describe your depth in each.
- Describe how you integrated governance tooling with cloud/lakehouse platforms, ETL/ELT, BI, ticketing and CI/CD processes.
- How have you defined governance and data-health KPIs/KRIs and demonstrated business value, risk reduction or control effectiveness to executives?
- Describe your experience with financial-services data risk or regulatory expectations such as BCBS 239, privacy, retention, auditability or regulatory reporting.
- Tell us about a major governance adoption challenge. How did you influence stakeholders and embed sustainable stewardship practices?
- What would your first 90-day approach be to assess the current state and establish a prioritized roadmap for this role?
