Data Quality Specialist

Title: Data Quality Specialist Location: Newark, NJ Duration: 12 months (potential to hire) Job Description 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 working with cross-functional control partners such as risk, compliance, legal, privacy, information security, model risk, internal audit, and business control teams. Ability to define metrics and dashboards that communicate control coverage, control effectiveness, exceptions, incidents, data quality health, AI data readiness, and remediation progress. Excellent written and verbal communication skills, with the ability to translate complex technical and governance concepts into clear guidance for executives, practitioners, and control partners. Strong execution and leadership skills, including backlog management, stakeholder alignment, decision documentation, operating model design, issue tracking, and delivery against milestones. Preferred Qualifications Experience in a regulated industry such as financial services, insurance, healthcare, or another environment with strong risk, privacy, security, and audit expectations. Experience with Data Quality, Responsible AI, AI governance, data governance, model risk management, technology risk, or operational risk frameworks. Working knowledge of data quality and observability tools, metadata/catalog platforms, lineage tooling, workflow tools, cloud platforms, issue management systems, and reporting/dashboarding tools. Experience designing DQ rule libraries, control catalogs, evidence schemas, certification criteria, policy mappings, AI data suitability checks, or automated control testing routines. Experience defining operating models, RACI, decision rights, adoption playbooks, metrics, and executive reporting routines. Technical fluency with SQL, Python, APIs, YAML/JSON configuration, rules engines, test automation, metadata definitions, or related engineering practices is strongly preferred. Bachelor's degree in computer science, data science, engineering, information systems, risk management, or a related field; advanced degree or relevant certifications preferred. Relevant Tools and Technology Exposure Data quality and data observability tools: Experience with platforms that support profiling, rule management, quality thresholds, anomaly detection, freshness monitoring, reconciliation, schema drift detection, issue management, and quality dashboards; examples include Ataccama ONE Data Quality, Informatica Data Quality, Collibra Data Quality, Soda, Monte Carlo, and Great Expectations or equivalent tools. Metadata, catalog, lineage, and data governance platforms: Working knowledge of cataloging, glossary management, metadata harvesting, lineage mapping, data ownership, stewardship workflows, data product certification, and policy mapping capabilities; examples include Informatica CDGC, Collibra, Microsoft Purview, Alation, OpenLineage, and related governance platforms. Responsible AI, AI governance, agentic AI, and ModelOps platforms: Familiarity with AI use case intake, model or agent registries, model risk workflows, evaluation tooling, monitoring, lifecycle governance, runtime guardrails, and AI control evidence; examples include IBM watsonx.governance, AWS AgentCore, AWS Guardrails, Azure AI Studio, Azure Machine Learning, MLflow, Databricks Mosaic AI, and model registry or monitoring platforms. Cloud, data platform, and lakehouse technologies: Familiarity with enterprise data and AI environments across modern cloud platforms, governed lakehouse architectures, warehouses, object storage, semantic layers, and pipeline-based data delivery; examples include AWS, Microsoft Azure, Snowflake, Databricks, Microsoft Fabric, and similar enterprise data platforms. Pipeline, orchestration, and automation tooling: Experience embedding governance and quality checks into data pipelines, workflow orchestration, CI/CD, monitoring routines, and automated evidence generation; examples include Airflow, Azure Data Factory, AWS Glue, dbt, GitHub, GitLab, Jenkins, and equivalent orchestration or DevOps tooling. Workflow, issue management, and reporting tools: Ability to use or partner on workflows, dashboards, scorecards, exception reporting, remediation tracking, and executive reporting to communicate control health and adoption progress; examples include ServiceNow, Jira, Power BI, Tableau, and similar workflow or reporting platforms. What Success Looks Like Data quality and AI data readiness requirements are embedded into delivery workflows, pipelines, certification routines, and platforms with clear ownership and measurable execution. AI products consume governed, traceable, fit-for-purpose, and appropriately controlled data from certified or approved sources. DQ and RAI control checks are increasingly automated, repeatable, traceable, and supported by audit-ready evidence. Control-plane dashboards provide timely visibility into data quality health, AI data readiness, risk posture, exceptions, incidents, remediation, and control effectiveness. Domain, product, engineering, and data teams understand what controls are required, how to implement them, and how to demonstrate compliance without unnecessary friction. Reusable DQ rules, evidence patterns, control libraries, and operating routines improve consistency, defensibility, and scalability across domains. Ideal Candidate Profile The ideal candidate is a senior hands-on lead who can bridge data architecture, data quality, data management, Responsible AI, and control operations. They are comfortable working with architects to identify control points, with engineers to automate DQ checks and evidence capture, with domain teams to implement rules and remediate issues, with AI and RAI teams to define data suitability expectations, and with risk and audit partners to demonstrate control effectiveness. They bring a practical mindset: governance should be embedded by design, measured through data, automated where possible, and easy for teams to adopt. #J-18808-Ljbffr Spectraforce Technologies