Data Quality and Responsible AI Governance Specialist

JSR Tech Consulting; Newark (NJ); On-site; USD 157,597,000 - 177,655,000; Full time

Job summary

JSR Tech Consulting is seeking a Data Quality and Responsible AI Specialist to lead enterprise data quality and AI governance initiatives in an onsite Newark environment. You will implement controls across data sources, pipelines, and AI use cases, ensuring data readiness and regulatory alignment.

The role requires experience embedding governance into data workflows, defining AI-ready data thresholds, and delivering audit-ready evidence.

Qualifications

• Minimum 5+ years in data quality, governance, or Responsible AI operations.
• Experience embedding controls into data pipelines and AI data readiness.
• Strong knowledge of data architecture, metadata, lineage, and platform governance.

Responsibilities

• Lead enterprise implementation of data quality and AI data readiness controls across data sources and AI use cases.
• Define what AI-ready data means, including quality thresholds, lineage, and access controls.
• Translate Responsible AI requirements into measurable data controls integrated into pipelines and dashboards.
• Collaborate with data architects, engineering, and domain teams to place controls across ingestion, transformation, and publication.
• Model data concepts to support trusted data products and ADS certification.
• Build reusable control patterns, evidence payloads, and implementation guidance.
• Guide teams on DQ rules, thresholds, metrics, and remediation.
• Maintain control libraries for data quality, metadata, lineage, and governance.

Skills

Data quality

Governance

Responsible AI

Data architecture

CI/CD integration

Education

Bachelor's degree in CS/related field

Tools

Ataccama

Informatica

Collibra

Power BI

Job description

Data Quality and Responsible AI Specialist

• Location: Newark, NJ (Onsite, 5 days per week)
• Employment Type: Long-term Contract to Hire
• 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
• 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