Data Steward

Roles and Responsibilities

The Enterprise Data Steward will support the development, implementation, and maintenance of data governance principles, policies, and processes. Close interaction and alignment with the respective group functions and tenant organizations to enable data governance execution is a key focus.

The job scope also includes:

• Support the business and technical data owners in the development of data management initiatives as per data governance guidelines.
• Support the business and technical data owners within different business units as per data governance guidelines, including defining the requirements, monitoring the progress, and developing reports about related KPIs.
• Ensure implementation of the defined governance policies, processes, and guidelines across the business units and monitor compliance.
• Work with DA business partners, data owners, and analytics and engineering teams to understand the critical business questions to be answered and the raw and derived data needs to support different users and use cases.
• Develop, own, and maintain data schemas, data dictionaries, data catalogs, document metadata, data lineage, and applied transformations.
• Define data transformations and business logic required to take datasets from their raw form into usable, high-quality assets.
• Establish data quality requirements, monitor data quality trends as per the defined standards, and work with data engineers and data producers to resolve and prevent data issues.
• Ensure adherence to data usage and access standards and compliance policies.

As an ideal candidate, you will need:

• Bachelor’s degree in information technology or a similar field or equivalent work experience accepted.
• Minimum of 5 years of relevant experience within the particular area of expertise.
• Domain knowledge of critical data sets, priority use cases, and understanding of business processes.
• Basic knowledge of how to define and implement data procedures and policies.
• Basic knowledge of data management principles and concepts.
• Basic knowledge and expertise in industry-leading data management practices.
• Basic knowledge of data & analytics cloud implementations and operations.
• Ability to break down complex problems and projects into manageable goals.
• Basic knowledge and experience in setting up and/or executing data governance in an organization.
• Working experience in a data catalogue platform.
• Good communication skills.

Desired Candidate Profile

An Enterprise Data Steward plays a critical role in managing and overseeing the data within an organization to ensure that it is accurate, consistent, secure, and accessible. They are responsible for implementing data governance policies, maintaining data quality standards, and ensuring that data is used effectively across the organization. The role is essential in ensuring that enterprise data aligns with business objectives, complies with relevant regulations, and supports data-driven decision-making.

Key Responsibilities of an Enterprise Data Steward:

1. Data Governance and Compliance

• Data Quality Management: Establish and monitor data quality standards to ensure that data is accurate, complete, and reliable. This involves identifying data issues, implementing corrective actions, and continuously improving data quality across the organization.
• Data Governance Framework: Implement and enforce a data governance framework, which includes establishing roles, responsibilities, policies, and procedures for managing data across the enterprise.
• Compliance with Regulations: Ensure that data management practices comply with relevant data privacy regulations, such as GDPR, CCPA, HIPAA, and other industry-specific regulations.
• Data Privacy: Work with legal and compliance teams to ensure that sensitive data is protected and that personal data is handled according to privacy policies and regulations.

2. Data Quality Assurance

• Monitoring Data Quality: Continuously assess the quality of data within the enterprise, including identifying any inconsistencies, errors, or redundancies. This includes using automated tools for data profiling, data cleansing, and monitoring.
• Data Cleansing: Identify and correct inaccurate or incomplete data, ensuring that data is consistent and reliable for business processes.
• Establishing Data Standards: Create and enforce standards for data entry, formatting, and storage across the organization to ensure consistency and compatibility across systems.

3. Data Lifecycle Management

• Data Integration: Ensure that data from different systems, departments, or business units is integrated correctly and maintain a consistent data flow across the organization.
• Data Archiving and Retention: Develop policies for data retention and archiving, ensuring that outdated or irrelevant data is properly archived or deleted in accordance with legal and business requirements.
• Data Classification: Establish and manage data classification schemes to categorize data by sensitivity, usage, or business value, making it easier to manage and access.