Data Quality and Responsible AI Governance Specialist

Job Description

Data Quality and Responsible AI Specialist

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

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• Sits at the intersection of Data Management and Governance, enterprise data quality, Responsible AI operations, data architecture, and technology risk management
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• Embeds quality and governance controls directly into data delivery workflows
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• Helps mature a control plane that provides visibility into AI data readiness, data quality health, control coverage, exceptions, remediation, and audit-ready evidence
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Key Responsibilities

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• Lead enterprise implementation of data quality and AI data readiness controls across data sources, data products, semantic products, and AI use cases
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• Define what "AI-ready data" means, including quality thresholds, lineage and metadata completeness, source authorization, classification, access controls, and freshness
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• Translate Responsible AI requirements into measurable data controls embedded in pipelines, certification workflows, metadata platforms, and dashboards
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• Partner with data architects, engineering, platform, and domain teams to place controls across ingestion, transformation, publication, AI consumption, and runtime monitoring
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• Perform hands-on conceptual, logical, physical, canonical, and semantic data modeling to support trusted data products and ADS certification
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• Build reusable DQ and RAI control patterns, rule templates, evidence payloads, and implementation guidance
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• Guide domain teams on DQ rules, thresholds, metrics, exceptions, and remediation
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• Establish routines for profiling, rule execution, issue triage, root-cause analysis, remediation tracking, and recertification
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• Integrate data quality controls into AI lifecycle gates
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• Maintain control libraries for data quality, metadata, lineage, access, privacy, monitoring, and lifecycle governance
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• Drive automation to reduce manual governance burden and improve audit readiness
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• Define KRIs, KPIs, alerts, and reporting that give senior leaders visibility into data quality health and remediation progress
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• Coordinate with business, product, engineering, security, privacy, legal, compliance, risk, model risk, and audit teams
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• Maintain audit-ready documentation including control mappings, rule logic, test results, approvals, and remediation evidence
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• Lead playbooks, standards, training, and enablement materials for DQ and RAI adoption at scale
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Required Skills and Experience

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• Strong experience in enterprise data quality, data governance, data management, data architecture, technology controls, or Responsible AI operations
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• Strong understanding of enterprise data architecture, data products, data contracts, metadata, lineage, semantic layers, and governed lakehouse or cloud platforms
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• Hands-on experience designing conceptual, logical, physical, canonical, dimensional, and semantic data models
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• Hands-on knowledge of data quality frameworks: rule design, profiling, thresholds, observability, reconciliation, anomaly detection, and scorecards
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• Ability to connect data quality outcomes to Responsible AI needs such as traceability, data suitability, bias and proxy risk, and privacy constraints
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• Experience embedding controls into pipelines, workflows, metadata systems, or CI/CD processes
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• Familiarity with AI/ML, generative AI, agentic AI, model lifecycle management, model registries, and production release controls
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• Ability to map policy and regulatory expectations into practical requirements, testing procedures, and evidence
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• Experience working with risk, compliance, legal, privacy, information security, model risk, and internal audit partners
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• Ability to build metrics and dashboards on control coverage, effectiveness, exceptions, and remediation
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• Excellent written and verbal communication skills, including with executives
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• Strong execution and leadership skills, including backlog management, stakeholder alignment, and delivery against milestones
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Preferred Qualifications

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• Experience in a regulated industry such as financial services, insurance, or healthcare
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• Experience with Responsible AI, AI governance, model risk management, technology risk, or operational risk frameworks
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• Experience designing DQ rule libraries, control catalogs, evidence schemas, and automated control testing
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• Experience defining operating models, RACI, decision rights, and executive reporting
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• Technical fluency with SQL, Python, APIs, YAML/JSON, rules engines, and test automation
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• Bachelor's degree in computer science, data science, engineering, information systems, risk management, or related field; advanced degree or certifications preferred
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Tools and Technology Exposure

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• Data Quality and Observability: Ataccama ONE, Informatica Data Quality, Collibra Data Quality, Soda, Monte Carlo, Great Expectations
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• Metadata, Catalog, and Lineage: Informatica CDGC, Collibra, Microsoft Purview, Alation, OpenLineage
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• Responsible AI and ModelOps: IBM watsonx.governance, AWS AgentCore, AWS Guardrails, Azure AI Studio, Azure Machine Learning, MLflow, Databricks Mosaic AI
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• Cloud and Data Platforms: AWS, Azure, Snowflake, Databricks, Microsoft Fabric
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• Pipeline and Orchestration: Airflow, Azure Data Factory, AWS Glue, dbt, GitHub, GitLab, Jenkins
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• Workflow and Reporting: ServiceNow, Jira, Power BI, Tableau
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What Success Looks Like

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• DQ and AI data readiness requirements are embedded into workflows, pipelines, and platforms with clear ownership
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• AI products consume governed, traceable, fit-for-purpose data from certified sources
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• DQ and RAI control checks are increasingly automated and backed by audit-ready evidence
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• Dashboards provide timely visibility into data quality health, risk posture, and remediation
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• Teams understand required controls and can demonstrate compliance without unnecessary friction
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Ideal Candidate

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• Senior, hands-on lead who bridges data architecture, data quality, Responsible AI, and control operations
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• Comfortable working with architects, engineers, domain teams, AI teams, and risk and audit partners
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• Practical mindset: governance embedded by design, measured through data, and automated where possible
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