Data Quality Analyst

Job Title: Data Quality & Responsible AI Governance Lead

Location: Newark, NJ (Hybrid — 3 days/week onsite)

Employment Type: Contract-to-Hire

Pay Rate: $55–$62/hour, depending on experience

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.

• Strong experience in enterprise data quality, data governance, data management, data architecture, technology controls, or Responsible AI operations…