Quality Assurance Specialist

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 sr. 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 CD Collibra, Microsoft Purview, Alation, OpenLineage, and related catalog or 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.
Technical languages, modeling, and configuration skills: Practical fluency with SQL and Python, hands-on data modeling, plus comfort reading or writing APIs, YAML, JSON, Git-based configuration, rule logic, metadata definitions, semantic definitions, and reusable control templates.
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 sr. 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.
Skills
Quality assurance, AI, Data, Governance
Top Skills Details
Quality assurance,AI,Data,Governance
Additional Skills & Qualifications
Focused on Data Quality Operations and AI Data Readiness.
Hands-on operational role.
Working within an enterprise control-plane environment.
Investigating incidents, alerts, and governance exceptions.
Defining severity levels, quality rules, and monitoring standards.
AI observability experience highly desired.
Ataccama experience strongly preferred.
Insurance preferred; Financial Services acceptable.
Experience Level
Intermediate Level
Job Type & Location
This is a Contract to Hire position based out of Newark, NJ.
Pay and Benefits
The pay range for this position is $55.00 - $65.00/hr.
Individual compensation offered for this position within this range will depend on many factors, including qualifications, skills, relevant experience, job knowledge, geographic location, internal equity, and other pertinent job-related factors.
Eligibility requirements apply to some benefits and may depend on your job classification and length of employment. Benefits are subject to change and may be subject to specific elections, plan, or program terms. If eligible, the benefits available for this temporary role may include the following: Medical, dental & vision Critical Illness, Accident, and Hospital 401(k) Retirement Plan - Pre-tax and Roth post-tax contributions available Life Insurance (Voluntary Life & AD&D for the employee and dependents) Short and long-term disability Health Spending Account (HSA) Transportation benefits Employee Assistance Program Time Off/Leave (PTO, Vacation or Sick Leave)
Workplace Type
This is a hybrid position in Newark,NJ.
Application Deadline
This position is anticipated to close on Sep 1, 2026.

About TEKsystems

We're partners in transformation. We help clients activate ideas and solutions to take advantage of a new world of opportunity. We are a team of 80,000 strong, working with over 6,000 clients, including 80% of the Fortune 500, across North America, Europe and Asia. As an industry leader in Full-Stack Technology Services, Talent Services, and real-world application, we work with progressive leaders to drive change. That's the power of true partnership. TEKsystems is an Allegis Group company.

The company is an equal opportunity employer and will consider all applications without regards to race, sex, age, color, religion, national origin, veteran status, disability, sexual orientation, gender identity, genetic information or any characteristic protected by law.

About TEKsystems and TEKsystems Global Services

We're a leading provider of business and technology services. We accelerate business transformation for our customers. Our expertise in strategy, design, execution and operations unlocks business value through a range of solutions. We're a team of 80,000 strong, working with over 6,000 customers, including 80% of the Fortune 500 across North America, Europe and Asia, who partner with us for our scale, full-stack capabilities and speed. We're strategic thinkers, hands-on collaborators, helping customers capitalize on change and master the momentum of technology. We're building tomorrow by delivering business outcomes and making positive impacts in our global communities. TEKsystems and TEKsystems Global Services are Allegis Group companies. Learn more at TEKsystems.com.

The company is an equal opportunity employer and will consider all applications without regard to race, sex, age, color, religion, national origin, veteran status, disability, sexual orientation, gender identity, genetic information or any characteristic protected by law.

San Francisco Fair Chance Ordinance: Pursuant to the San Francisco Fair Chance Ordinance, for all positions located in the city and county of San Francisco, we will consider for employment qualified applicants with arrest and conviction records.

Massachusetts Lie Detector: It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

Use of Artificial Intelligence (AI): We may use Artificial Intelligence (AI) to support parts of our hiring process, including sourcing, screening, and evaluating candidates. AI helps assess applications and qualifications, but final decisions are made by our hiring team. By applying, you acknowledge and agree that your application may be reviewed using AI tools.