Sits at the intersection of Data Management and Governance, enterprise data quality, Responsible AI operations, data architecture, and technology risk management
Embeds quality and governance controls directly into data delivery workflows
Helps mature a control plane that provides visibility into AI data readiness, data quality health, control coverage, exceptions, remediation, and audit-ready evidence
Key Responsibilities
Lead enterprise implementation of data quality and AI data readiness controls across data sources, data products, semantic products, and AI use cases
Define what "AI-ready data" means, including quality thresholds, lineage and metadata completeness, source authorization, classification, access controls, and freshness
Translate Responsible AI requirements into measurable data controls embedded in pipelines, certification workflows, metadata platforms, and dashboards
Partner with data architects, engineering, platform, and domain teams to place controls across ingestion, transformation, publication, AI consumption, and runtime monitoring
Perform hands-on conceptual, logical, physical, canonical, and semantic data modeling to support trusted data products and ADS certification
Build reusable DQ and RAI control patterns, rule templates, evidence payloads, and implementation guidance
Guide domain teams on DQ rules, thresholds, metrics, exceptions, and remediation
Establish routines for profiling, rule execution, issue triage, root-cause analysis, remediation tracking, and recertification
Integrate data quality controls into AI lifecycle gates
Maintain control libraries for data quality, metadata, lineage, access, privacy, monitoring, and lifecycle governance
Drive automation to reduce manual governance burden and improve audit readiness
Define KRIs, KPIs, alerts, and reporting that give senior leaders visibility into data quality health and remediation progress
Coordinate with business, product, engineering, security, privacy, legal, compliance, risk, model risk, and audit teams
Maintain audit-ready documentation including control mappings, rule logic, test results, approvals, and remediation evidence
Lead playbooks, standards, training, and enablement materials for DQ and RAI adoption at scale
Required Skills and Experience
Strong experience in enterprise data quality, data governance, data management, data architecture, technology controls, or Responsible AI operations
Strong understanding of enterprise data architecture, data products, data contracts, metadata, lineage, semantic layers, and governed lakehouse or cloud platforms
Hands-on experience designing conceptual, logical, physical, canonical, dimensional, and semantic data models
Hands-on knowledge of data quality frameworks: rule design, profiling, thresholds, observability, reconciliation, anomaly detection, and scorecards
Ability to connect data quality outcomes to Responsible AI needs such as traceability, data suitability, bias and proxy risk, and privacy constraints
Experience embedding controls into pipelines, workflows, metadata systems, or CI/CD processes
Familiarity with AI/ML, generative AI, agentic AI, model lifecycle management, model registries, and production release controls
Ability to map policy and regulatory expectations into practical requirements, testing procedures, and evidence
Experience working with risk, compliance, legal, privacy, information security, model risk, and internal audit partners
Ability to build metrics and dashboards on control coverage, effectiveness, exceptions, and remediation
Excellent written and verbal communication skills, including with executives
Strong execution and leadership skills, including backlog management, stakeholder alignment, and delivery against milestones
Preferred Qualifications
Experience in a regulated industry such as financial services, insurance, or healthcare
Experience with Responsible AI, AI governance, model risk management, technology risk, or operational risk frameworks
Experience designing DQ rule libraries, control catalogs, evidence schemas, and automated control testing
Experience defining operating models, RACI, decision rights, and executive reporting
Technical fluency with SQL, Python, APIs, YAML/JSON, rules engines, and test automation
Bachelor's degree in computer science, data science, engineering, information systems, risk management, or related field; advanced degree or certifications preferred
Tools and Technology Exposure
Data Quality and Observability: Ataccama ONE, Informatica Data Quality, Collibra Data Quality, Soda, Monte Carlo, Great Expectations
Metadata, Catalog, and Lineage: Informatica CDGC, Collibra, Microsoft Purview, Alation, OpenLineage
Responsible AI and ModelOps: IBM watsonx.governance, AWS AgentCore, AWS Guardrails, Azure AI Studio, Azure Machine Learning, MLflow, Databricks Mosaic AI
Cloud and Data Platforms: AWS, Azure, Snowflake, Databricks, Microsoft Fabric
Pipeline and Orchestration: Airflow, Azure Data Factory, AWS Glue, dbt, GitHub, GitLab, Jenkins
Workflow and Reporting: ServiceNow, Jira, Power BI, Tableau
What Success Looks Like
DQ and AI data readiness requirements are embedded into workflows, pipelines, and platforms with clear ownership
AI products consume governed, traceable, fit-for-purpose data from certified sources
DQ and RAI control checks are increasingly automated and backed by audit-ready evidence
Dashboards provide timely visibility into data quality health, risk posture, and remediation
Teams understand required controls and can demonstrate compliance without unnecessary friction
Ideal Candidate
Senior, hands-on lead who bridges data architecture, data quality, Responsible AI, and control operations
Comfortable working with architects, engineers, domain teams, AI teams, and risk and audit partners
Practical mindset: governance embedded by design, measured through data, and automated where possible
