Data Quality and Responsible AI Governance Specialist

Data Quality and Responsible AI Specialist

• Location: Newark, NJ (Onsite, 5 days per week)
• Employment Type: Long-term Contract to Hire
• Client: Major Financial Firm
• Pay Rate: $55 to $62 per hour on W2
• No C2C. W2 only.

About the Role

• 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