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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Quality Analyst - **Company:** JSR Tech Consulting - **Location:** East Orange, NJ, United States - **Salary:** $114,400.0 - $128,960.0 - **Contract:** Permanent contract - **Skills:** Training Data, Application Programming Interfaces (APIs), Artificial Intelligence, Automation of Tests, Profiling, Encodings, Cyber Security, Information Systems, Continuous Integration, Data Architecture, Data Control, Information Engineering, Data Governance, JSON, Python (Programming Language), Metadata, SQL Databases, YAML, Enterprise Data Management, Cloud Platform System, Grafana, Model Validation, Generative AI, Agentic-AI, Data Layers, Information Technology, Data Management, Machine Learning Operations, Tools for Reporting - **Published:** October 9, 2026 - **Apply:** https://www.disabledperson.com/jobs/75933178-data-quality-analyst ## About the Role * Strong experience in enterprise data quality, data governance, data management, data architecture, technology controls, or Responsible AI operations within a complex enterprise environment. * Strong understanding of enterprise data architecture, authorized data sources, data products, data contracts, metadata, lineage, semantic layers, access controls, and governed lakehouse/cloud data platform patterns. * Hands-on experience designing/reviewing conceptual, logical, physical, canonical, dimensional, domain, and semantic data models. * Hands-on knowledge of data quality frameworks: rule design, profiling, thresholds, observability, reconciliation, anomaly detection, issue management, remediation, and scorecards. * Ability to connect data quality outcomes to Responsible AI needs: traceability, suitability, representativeness, bias/proxy-risk, privacy, monitoring, and lifecycle governance. * Experience embedding controls into pipelines, workflows, platforms, certification routines, metadata systems, or CI/CD. * Familiarity with AI/ML, generative AI, agentic AI, model lifecycle management, model registries, evaluation workflows, and production release controls. * Ability to translate policy and regulatory expectations into practical requirements, acceptance criteria, and evidence expectations. * Experience partnering with risk, compliance, legal, privacy, information security, model risk, and internal audit. * Excellent written and verbal communication skills for executives, practitioners, and control partners. * Strong execution and leadership skills: backlog management, stakeholder alignment, operating model design, and delivery against milestones. Preferred Qualifications * Experience in a regulated industry (financial services, insurance, healthcare, or similar). * Experience with Data Quality, Responsible AI, AI governance, model risk, technology risk, or operational risk frameworks. * Working knowledge of DQ/observability tools, metadata/catalog platforms, lineage tooling, cloud platforms, and reporting tools. * Experience designing DQ rule libraries, control catalogs, evidence schemas, certification criteria, or automated control testing. * Experience defining operating models, RACI, decision rights, and executive reporting routines. * Technical fluency with SQL, Python, APIs, YAML/JSON, rules engines, and test automation strongly preferred. * Bachelor's degree in computer science, data science, engineering, information systems, risk management, or a related field; advanced degree or certifications preferred. ## Description A major financial services firm is seeking a Data Quality & Responsible AI Governance Lead for a long-term, contract-to-hire engagement based in Newark, NJ (3 days/week onsite). This role sits at the intersection of data management and governance, enterprise data quality assurance, Responsible AI operations, data architecture, and technology risk management. You will make quality and governance requirements executable within the flow of delivery by embedding controls into data sourcing, ADS and data product certification, metadata and lineage workflows, pipeline validation, AI lifecycle gates, monitoring, exception management, remediation, recertification, and evidence generation. You will help mature a control plane that provides visibility into AI data readiness, data quality health, control coverage, exceptions, incidents, remediation status, and audit-ready evidence., * Lead enterprise implementation of data quality and AI data readiness controls across authorized data sources, data products, semantic products, and AI use cases. * Define what "AI-ready data" means in practice, including quality thresholds, lineage completeness, metadata completeness, source authorization, classification, access controls, issue history, freshness, and remediation expectations. * Translate Responsible AI control requirements into measurable data control requirements embedded into pipelines, certification workflows, metadata platforms, dashboards, and evidence routines. * Partner with data architects, data engineering, platform, and domain teams to determine where controls belong across ingestion, transformation, publication, semantic access, AI consumption, and runtime monitoring. * Perform hands-on data modeling (conceptual, logical, physical, canonical, semantic) to support trusted data products, ADS certification, AI consumption patterns, and downstream DQ control design. * Define reusable DQ and RAI control patterns, rule templates, evidence payloads, and implementation guidance that domain teams can adopt consistently. * Guide domain teams on defining DQ rules, thresholds, exception management, remediation, and evidence without duplicating central governance processes. * Establish operating routines for profiling, rule execution, exception review, root-cause analysis, remediation tracking, retesting, recertification, and closure evidence. * Integrate data quality controls into AI lifecycle gates so AI products use fit-for-purpose, authorized, governed, and traceable data sources. * Define and maintain control libraries for data quality, AI data readiness, metadata, lineage, access, privacy, monitoring, certification, and lifecycle governance. * Drive automation that reduces manual governance burden while improving traceability, repeatability, and audit readiness. * Define monitoring thresholds, alerts, KRIs, KPIs, and reporting routines that give senior leaders visibility into data quality health, AI readiness, exceptions, and remediation progress. * Coordinate across business owners, product teams, architecture, security, privacy, legal, compliance, risk, model risk, and audit. * Maintain audit-ready documentation including control mappings, rule logic, test results, approvals, exceptions, incidents, and remediation evidence. * Lead playbooks, standards, and enablement materials that help teams adopt DQ and RAI control practices at scale.