Specialist, Data Quality & Responsible AI Control Operations

Role Summary The Specialist, Data Quality & Responsible AI Control Operations is a hands-on role responsible for implementing, maintaining, and improving data quality and AI data readiness controls across authorized data sources, data products, and AI use cases. This role supports the connection between data architecture, hands-on data modeling, data management, Responsible AI, and control operations to help ensure AI products consume governed, traceable, fit-for-purpose, and high-quality data. The ideal candidate brings practical experience in data quality engineering, data modeling, data governance, metadata, lineage, control execution, and automation, with the ability to work closely with domain, technology, risk, compliance, audit, and AI governance stakeholders.

Team Context

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 supports execution of quality and governance requirements in the flow of delivery by helping embed 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 contribute to control-plane capabilities that provide visibility into AI data readiness, data quality health, control coverage, exceptions, incidents, remediation status, and audit-ready evidence.

Key Responsibilities

- Implement data quality and AI data readiness controls across authorized data sources, data products, semantic products, and AI use cases.
- Support definition and application of AI-ready data criteria, including quality thresholds, lineage completeness, metadata completeness, source authorization, classification, access controls, issue history, freshness, and remediation expectations.
- Translate Responsible AI control requirements into practical data control tasks, test cases, rule logic, evidence requirements, and implementation steps.
- Partner with data architects, data engineering, platform, and domain teams to implement controls 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.
- Configure and maintain DQ rules, thresholds, evidence payloads, control templates, metadata mappings, and implementation artifacts using approved patterns.
- Work with domain teams to define DQ rules, set thresholds, emit raw DQ metrics, manage exceptions, remediate issues, and provide evidence in alignment with central governance expectations.
- Execute recurring data profiling, rule runs, exception reviews, issue triage, root-cause analysis support, remediation tracking, retesting, recertification, and closure evidence.
- Support integration of data quality controls into AI lifecycle gates so AI products use fit-for-purpose, authorized, governed, traceable, and appropriately controlled data sources.
- Maintain control library entries for data quality, AI data readiness, metadata, lineage, access, privacy, monitoring, certification, and lifecycle governance.
- Identify and implement automation opportunities that reduce manual governance effort while improving traceability, repeatability, defensibility, and audit readiness.
- Build and maintain monitoring thresholds, alerts, KRIs, KPIs, control effectiveness measures, dashboards, and reports that show data quality health, AI data readiness, exceptions, and remediation progress.
- Coordinate with business owners, product teams, data domains, platform engineering, architecture, security, privacy, legal, compliance, risk, model risk,



and audit to support 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 inputs.
- Contribute to playbooks, standards, implementation guidance, training, and enablement materials that help business and technology teams adopt DQ and RAI control practices.

Required Skills and Experience
- Practical 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.
- Good 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, reviewing, or maintaining 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 practices, 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 implementing controls in 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 convert policy, regulatory, and control expectations into practical tasks, acceptance criteria, testing steps, operating routines, and evidence artifacts.
- 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 support metrics and dashboards that communicate control coverage, control effectiveness, exceptions, incidents, data quality health, AI data readiness, and remediation progress.
- Strong written and verbal communication skills, with the ability to explain technical and governance concepts clearly to practitioners, stakeholders, and control partners.
- Strong execution skills, including backlog support, stakeholder follow-up, decision documentation, issue tracking, control testing, and delivery against milestones.

Preferred Qualifications
- Experience in a regulated industry such as financial services, insurance, healthcare, or another environment with solid 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 supporting DQ rule libraries, control catalogs, evidence schemas, certification criteria, policy mappings, AI data suitability checks, or automated control testing routines.
- Experience supporting operating models, RACI,



decision rights, adoption playbooks, metrics, and executive or management reporting routines.
- Technical fluency with SQL, Python, APIs, YAML/JSON configuration, rules engines, test automation, metadata definitions, data modeling artifacts, or related engineering practices is strongly preferred.
- Bachelors degree in computer science, data science, engineering, information systems, risk management, or a related field; 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 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 controls are implemented accurately in delivery workflows, pipelines, certification routines, and platforms.
- AI products are supported by governed, traceable, fit-for-purpose, and appropriately controlled data from certified or approved sources.
- DQ and RAI control checks are executed consistently and supported by clear, repeatable, audit-ready evidence.
- Dashboards and reports provide timely visibility into data quality health, AI data readiness, exceptions, incidents, remediation, and control effectiveness.
- Domain, product, engineering, and data teams receive practical support on how to implement controls and demonstrate compliance without unnecessary friction.
- DQ rules, evidence patterns, control library updates, modeling artifacts, and operating routines are maintained with accuracy, consistency, and traceability.

📌 Data Quality Lead (Gurugram)
🏢 Cognizant
📍 Gurugram