Data Governance & Data Management Specialist
Visionet Systems · Bengaluru · onsite · Posted 61d ago
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About this role
Job Description - Data Governance & Data Management Specialist
Experience
7-10 Years
Role Overview
We are looking for an experienced Data Governance & Data Management Specialist with strong hands-on expertise in data governance, data quality, metadata management, data lineage, MDM, and modern cloud data platforms.
The ideal candidate will combine data governance strategy with practical implementation skills, working closely with business, technology, data engineering, security, and compliance teams to establish governance standards and deliver high-quality, trusted, and compliant data assets.
The candidate should also have exposure to Agentic AI and understand how governance, metadata, data quality, and security can support enterprise AI initiatives.
Key Responsibilities
1. Data Governance & Management
- Implement and operationalize enterprise Data Governance frameworks, policies, standards, and controls.
- Establish data governance standards, principles, guardrails, processes, and operating models.
- Define and maintain data ownership, stewardship, accountability, and governance roles.
- Develop governance processes aligned with business, regulatory, privacy, and compliance requirements.
- Work with business and technology stakeholders to identify critical data elements and establish governance controls.
- Support implementation of Data Governance Operating Models across business domains.
2. Data Catalog, Metadata & Lineage
- Hands-on experience with governance platforms such as Informatica, Collibra, or equivalent tools.
- Build and maintain enterprise data catalogs, business glossaries, technical metadata, and data dictionaries.
- Implement and manage end-to-end data lineage across source systems, data platforms, transformations, and consumption layers.
- Define metadata management processes and standards.
- Establish relationships between business terms, technical assets, data owners, policies, and data quality rules.
- Support metadata-driven governance and data discovery initiatives.
3. Data Quality
- Define and implement Data Quality (DQ) frameworks, rules, controls, and KPIs.
- Establish DQ dimensions including accuracy, completeness, consistency, timeliness, uniqueness, and validity.
- Develop and execute data quality rules using Informatica, Collibra, Snowflake, SQL, or equivalent technologies.
- Analyze DQ issues, identify root causes, and coordinate remediation with data owners and engineering teams.
- Develop DQ dashboards, scorecards, exception reports, and remediation processes.
- Establish continuous data quality monitoring and improvement mechanisms.
4. Master Data Management
- Practical experience with Master Data Management (MDM) concepts and implementation.
- Define and implement master data governance processes for domains such as Customer, Product, Supplier, Location, Employee, etc.
- Understand reference data, golden records, survivorship rules, matching, merging, and hierarchy management.
- Work with business data owners and stewards to establish master data standards.
- Support MDM integration with enterprise data platforms and downstream applications.
5. Snowflake & Modern Data Architecture
- Strong understanding of Snowflake architecture and capabilities.
- Good understanding of modern data architecture patterns, particularly Medallion Architecture - Bronze, Silver, and Gold layers.
- Understand how data governance, metadata, lineage, DQ, security, and access controls are implemented across modern data platforms.
- Working knowledge of SQL and ability to perform data analysis, profiling, validation, and DQ investigations in Snowflake.
- Understand data sharing, role-based access, data security, and governance considerations within Snowflake environments.
6. Agentic AI & Emerging Technologies
- Exposure to Agentic AI initiatives, AI agents, GenAI, or enterprise AI use cases.
- Understand the importance of data governance, metadata, data quality, security, and responsible AI for Agentic AI solutions.
- Participate in identifying and implementing governance patterns for AI-ready and AI-consumable data.
- Support use cases involving intelligent data discovery, automated DQ remediation, metadata enrichment, data classification, or AI-powered governance.
7. Technical Delivery & Implementation
- Take a hands-on approach to implementing governance and data management capabilities.
- Translate governance requirements into technical solutions, workflows, rules, and controls.
- Work closely with Data Engineers, Architects, Business Analysts, Data Stewards, Security, and Compliance teams.
- Participate in solution design, configuration, development, testing, deployment, and operationalization.
- Create technical and functional documentation, standards, procedures, and implementation guides.
- Troubleshoot data, metadata, lineage, and quality issues and drive them through resolution.
Required Technical Skills
Area
Required Skills
Data Governance
Governance frameworks, policies, standards, guardrails, operating models
Data Catalog
Informatica, Collibra, or equivalent
Metadata
Business & technical metadata, glossary, metadata management
Data Lineage
End-to-end lineage, impact analysis, dependency mapping
Data Quality
DQ rules, profiling, scorecards, monitoring, remediation
MDM
Golden record, matching, survivorship, hierarchy, stewardship
Snowflake
Architecture, SQL, security, governance, Medallion Architecture
Data Architecture
Bronze/Silver/Gold, Data Lake/Lakehouse concepts
AI
Agentic AI, GenAI, AI governance, AI-ready data
Technical Skills
SQL, data analysis, profiling, scripting/automation
Governance & Compliance
Data classification, privacy, regulatory controls, access governance
Preferred Skills
- Informatica Data Quality / Informatica Axon / Informatica Cloud Data Governance.
- Collibra Data Intelligence Platform.
- Experience with Microsoft Azure / Microsoft Fabric or other modern cloud data platforms.
- Experience with Power BI or other data visualization platforms.
- Knowledge of Data Mesh, Data Products, Data Contracts, and domain-oriented governance.
- Experience implementing governance in large enterprise environments.
- Knowledge of Responsible AI and AI governance frameworks.
- Experience with Agile delivery methodologies and DevOps practices.
Key Competencies
- Strong analytical and problem-solving skills.
- Ability to translate business requirements into practical governance solutions.
- Strong stakeholder management and communication skills.
- Ability to work across business and technology teams.
- Strong ownership and execution mindset.
- Comfortable working hands-on with data, tools, SQL, metadata, and governance platforms.
- Ability to establish governance without creating unnecessary operational complexity.
- Strong documentation and presentation skills.
Education
- Bachelors or Masters degree in Computer Science, Information Technology, Data Management, Engineering, or a related discipline.
- Relevant certifications in Data Governance, Data Management, Informatica, Collibra, Snowflake, or cloud technologies are desirable.
Expected Experience Profile
The ideal candidate should demonstrate:
- 7-10 years of experience in Data Management, Data Governance, Data Quality, or related data disciplines.
- Proven hands-on implementation experience with at least one enterprise governance platform such as Informatica or Collibra.
- Practical experience implementing catalog, metadata, lineage, DQ, and MDM capabilities.
- Strong understanding of modern data platforms and Snowflake.
- Exposure to Agentic AI / GenAI initiatives.
- Ability to independently drive governance initiatives from requirements design implementation operationalization.
Job Description - Data Governance & Data Management Specialist
Experience
7-10 Years
Role Overview
We are looking for an experienced Data Governance & Data Management Specialist with strong hands-on expertise in data governance, data quality, metadata management, data lineage, MDM, and modern cloud data platforms.
The ideal candidate will combine data governance strategy with practical implementation skills, working closely with business, technology, data engineering, security, and compliance teams to establish governance standards and deliver high-quality, trusted, and compliant data assets.
The candidate should also have exposure to Agentic AI and understand how governance, metadata, data quality, and security can support enterprise AI initiatives.
Key Responsibilities
1. Data Governance & Management
- Implement and operationalize enterprise Data Governance frameworks, policies, standards, and controls.
- Establish data governance standards, principles, guardrails, processes, and operating models.
- Define and maintain data ownership, stewardship, accountability, and governance roles.
- Develop governance processes aligned with business, regulatory, privacy, and compliance requirements.
- Work with business and technology stakeholders to identify critical data elements and establish governance controls.
- Support implementation of Data Governance Operating Models across business domains.
2. Data Catalog, Metadata & Lineage
- Hands-on experience with governance platforms such as Informatica, Collibra, or equivalent tools.
- Build and maintain enterprise data catalogs, business glossaries, technical metadata, and data dictionaries.
- Implement and manage end-to-end data lineage across source systems, data platforms, transformations, and consumption layers.
- Define metadata management processes and standards.
- Establish relationships between business terms, technical assets, data owners, policies, and data quality rules.
- Support metadata-driven governance and data discovery initiatives.
3. Data Quality
- Define and implement Data Quality (DQ) frameworks, rules, controls, and KPIs.
- Establish DQ dimensions including accuracy, completeness, consistency, timeliness, uniqueness, and validity.
- Develop and execute data quality rules using Informatica, Collibra, Snowflake, SQL, or equivalent technologies.
- Analyze DQ issues, identify root causes, and coordinate remediation with data owners and engineering teams.
- Develop DQ dashboards, scorecards, exception reports, and remediation processes.
- Establish continuous data quality monitoring and improvement mechanisms.
4. Master Data Management
- Practical experience with Master Data Management (MDM) concepts and implementation.
- Define and implement master data governance processes for domains such as Customer, Product, Supplier, Location, Employee, etc.
- Understand reference data, golden records, survivorship rules, matching, merging, and hierarchy management.
- Work with business data owners and stewards to establish master data standards.
- Support MDM integration with enterprise data platforms and downstream applications.
5. Snowflake & Modern Data Architecture
- Strong understanding of Snowflake architecture and capabilities.
- Good understanding of modern data architecture patterns, particularly Medallion Architecture - Bronze, Silver, and Gold layers.
- Understand how data governance, metadata, lineage, DQ, security, and access controls are implemented across modern data platforms.
- Working knowledge of SQL and ability to perform data analysis, profiling, validation, and DQ investigations in Snowflake.
- Understand data sharing, role-based access, data security, and governance considerations within Snowflake environments.
6. Agentic AI & Emerging Technologies
- Exposure to Agentic AI initiatives, AI agents, GenAI, or enterprise AI use cases.
- Understand the importance of data governance, metadata, data quality, security, and responsible AI for Agentic AI solutions.
- Participate in identifying and implementing governance patterns for AI-ready and AI-consumable data.
- Support use cases involving intelligent data discovery, automated DQ remediation, metadata enrichment, data classification, or AI-powered governance.
7. Technical Delivery & Implementation
- Take a hands-on approach to implementing governance and data management capabilities.
- Translate governance requirements into technical solutions, workflows, rules, and controls.
- Work closely with Data Engineers, Architects, Business Analysts, Data Stewards, Security, and Compliance teams.
- Participate in solution design, configuration, development, testing, deployment, and operationalization.
- Create technical and functional documentation, standards, procedures, and implementation guides.
- Troubleshoot data, metadata, lineage, and quality issues and drive them through resolution.
Required Technical Skills
Area
Required Skills
Data Governance
Governance frameworks, policies, standards, guardrails, operating models
Data Catalog
Informatica, Collibra, or equivalent
Metadata
Business & technical metadata, glossary, metadata management
Data Lineage
End-to-end lineage, impact analysis, dependency mapping
Data Quality
DQ rules, profiling, scorecards, monitoring, remediation
MDM
Golden record, matching, survivorship, hierarchy, stewardship
Snowflake
Architecture, SQL, security, governance, Medallion Architecture
Data Architecture
Bronze/Silver/Gold, Data Lake/Lakehouse concepts
AI
Agentic AI, GenAI, AI governance, AI-ready data
Technical Skills
SQL, data analysis, profiling, scripting/automation
Governance & Compliance
Data classification, privacy, regulatory controls, access governance
Preferred Skills
- Informatica Data Quality / Informatica Axon / Informatica Cloud Data Governance.
- Collibra Data Intelligence Platform.
- Experience with Microsoft Azure / Microsoft Fabric or other modern cloud data platforms.
- Experience with Power BI or other data visualization platforms.
- Knowledge of Data Mesh, Data Products, Data Contracts, and domain-oriented governance.
- Experience implementing governance in large enterprise environments.
- Knowledge of Responsible AI and AI governance frameworks.
- Experience with Agile delivery methodologies and DevOps practices.
Key Competencies
- Strong analytical and problem-solving skills.
- Ability to translate business requirements into practical governance solutions.
- Strong stakeholder management and communication skills.
- Ability to work across business and technology teams.
- Strong ownership and execution mindset.
- Comfortable working hands-on with data, tools, SQL, metadata, and governance platforms.
- Ability to establish governance without creating unnecessary operational complexity.
- Strong documentation and presentation skills.
Education
- Bachelors or Masters degree in Computer Science, Information Technology, Data Management, Engineering, or a related discipline.
- Relevant certifications in Data Governance, Data Management, Informatica, Collibra, Snowflake, or cloud technologies are desirable.
Expected Experience Profile
The ideal candidate should demonstrate:
- 7-10 years of experience in Data Management, Data Governance, Data Quality, or related data disciplines.
- Proven hands-on implementation experience with at least one enterprise governance platform such as Informatica or Collibra.
- Practical experience implementing catalog, metadata, lineage, DQ, and MDM capabilities.
- Strong understanding of modern data platforms and Snowflake.
- Exposure to Agentic AI / GenAI initiatives.
- Ability to independently drive governance initiatives from requirements design implementation operationalization.
Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.
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About Visionet Systems
Visionet Systems
IT Services & Consulting
2.2k+
employees
1995
31 years old
Bangalore / Bengaluru, Karnataka
India
₹5.2L PA avg
Avg at Visionet Systems
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