Join Our Team: Senior Data Governance Specialist at NTT DATA

Are you passionate about data governance and looking to make an impact? NTT DATA is seeking a dedicated Senior Data Governance Specialist to design and develop our data infrastructure. If you enjoy leveraging your expertise to introduce innovative solutions and improve existing ones, this role is perfect for you.

As a Senior Data Governance Specialist, you will play a crucial role in shaping our data governance strategy and implementing data governance frameworks, policies, and procedures to establish best practices across NTT.

Key Roles and Responsibilities

Stakeholder Collaboration: Network with various stakeholders to analyze data requirements, develop data models, and ensure the integrity and security of our data infrastructure.

Data Management: Define how data will be stored, consumed, integrated, and managed by different data entities, IT systems, and applications.

Governance Framework: Create a data governance framework that meets business and technology requirements while ensuring data security and compliance with regulations.

Cross-Functional Work: Collaborate across multiple teams to optimize data storage and retrieval, enhance data quality, and guide informed data-driven decisions.

Data Integration: Design and implement data integration and data flow processes to ensure efficient data movement across different systems and platforms.

Data Architecture: Analyze, plan, and define data architecture frameworks, including security, reference data, metadata, and master data.

Policy Implementation: Develop and implement data governance policies and procedures to maintain data accuracy, consistency, and security.

Community Engagement: Plan and facilitate data governance communities, workshops, and meetings across multiple teams.

Training and Development: Develop and maintain data governance training setups, ensuring data governance roles and relevant business partners are trained and onboarded.

Data Standards: Define and maintain data standards, data definitions, and data dictionaries to ensure consistency and interoperability.

Technical Standards: Define technical standards and guidelines for data use, security, access, and governance.

Business Vocabulary: Define a standard common business vocabulary, express strategic data requirements, and outline high-level integrated designs that align with enterprise strategy.

Data Diagrams: Create diagrams showing key data entities and generate an inventory of the data needed to implement the architecture vision.

Knowledge, Skills, and Attributes

Data Governance Expertise: Strong understanding of data governance, data integration, data quality, data lifecycle processes, and data management best practices.

Business Acumen: Enterprise-wide view of the business, strong appreciation for strategy, processes, capabilities, technologies, and governance.

Communication Skills: Proficient in preparing, facilitating, and driving forums, workshops, and meetings across various organizational levels.

Change Management: Skillful change agent and communicator who can demonstrate the value of data governance, create organizational buy-in, and articulate the significance of data governance.

Collaboration: Ability to collaborate and communicate effectively with team members, contributing to their success.

Relationship Building: Ability to build relationships, manage business partners, and navigate effortlessly at all organizational levels from senior management to specialists.

Training and Communication: Expertise in developing communication and training materials and training people in data governance.

Technical Knowledge: Strong understanding of IT domains such as information security, application, technology infrastructure, and integration.

Problem-Solving Skills: Analytical capabilities to safeguard data integrity, security, and organizational compliance with regulations.

Data Security: Strong understanding of data security and privacy regulations (e.g., GDPR, CCPA) and experience implementing data protection measures.

Data Modelling: Expertise in data modelling techniques, data warehousing concepts, and database management systems.

SQL Proficiency: Advanced skills in SQL and experience with database development and performance optimization.