Quality Assurance Specialist

Long term, contract to hire position with a major financial firm. 5 days / week onsite in Newark, NJ. This role sits at the intersection of Data Management & Governance, enterprise data quality assurance, Responsible AI operations, data architecture, and technology risk management. The position is accountable for making quality and governance requirements executable in 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. The role 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. Key Responsibilities 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 that can be embedded into data 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 across conceptual, logical, physical, canonical, and semantic models 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, operating routines, and implementation guidance that domain teams can adopt consistently. Guide domain teams on defining DQ rules, setting thresholds, emitting raw DQ metrics, managing exceptions, remediating issues, and providing evidence without duplicating central governance processes. Establish operating routines for recurring data profiling, rule execution, exception review, issue triage, 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, traceable, and appropriately controlled data sources. Define and maintain control libraries for data quality, AI data readiness, metadata, lineage, access, privacy, monitoring, certification, and lifecycle governance. Drive automation opportunities that reduce manual governance burden while improving traceability, repeatability, defensibility, and audit readiness. Define monitoring thresholds, alerts, KRIs, KPIs, control effectiveness measures, and reporting routines that provide senior leaders visibility into data quality health, AI data readiness, exceptions, and remediation progress. Coordinate across business owners, product teams, data domains, platform engineering, architecture, security, privacy, legal, compliance, risk, model risk, and audit to ensure consistent execution of control requirements. Maintain audit‑ready documentation, including control mappings, rule logic, test results, workflow decisions, approvals, exceptions, incident records, remediation evidence, and management reporting. Lead playbooks, standards, implementation guidance, training, and enablement materials that help business and technology teams adopt DQ and RAI control practices at scale. Required Skills and Experience Strong experience in enterprise data quality, data governance, data management, data architecture, technology controls, Responsible AI operations, or a closely related discipline within a complex enterprise environment. Strong understanding of enterprise data architecture, hands‑on data modeling, authorized data sources, data products, data contracts, metadata, lineage, semantic layers, access controls, and governed lakehouse or cloud data platform patterns. Hands‑on experience designing and reviewing conceptual, logical, physical, canonical, dimensional, domain, and semantic data models, including entity relationships, critical data elements, business definitions, data contracts, and AI‑consumable semantic structures. Hands‑on knowledge of data quality frameworks, including rule design, profiling, thresholds, data observability, reconciliation, anomaly detection, issue management, remediation, and quality scorecards. Ability to connect data quality outcomes to Responsible AI control needs, including traceability, data suitability, representativeness, bias/proxy‑risk considerations, privacy constraints, monitoring, and lifecycle governance. Experience embedding controls into pipelines, workflows, platforms, certification routines, metadata systems, or CI/CD processes rather than relying solely on manual review. Familiarity with AI/ML, generative AI, agentic AI, model lifecycle management, model registries, evaluation workflows, monitoring, and production release controls. Ability to map policy, regulatory, and control expectations into practical requirements, acceptance criteria, testing procedures, operating routines, and evidence expectations. Experience #J-18808-Ljbffr