Data Governance Lead

At Tiger Analytics, the Data Governance Lead plays a crucial role in developing a comprehensive data governance framework, which includes creating policies, standards, and best practices. This position involves defining roles and ensuring accountability within the organization, establishing metrics for data quality, and leading initiatives to resolve discrepancies in data. Collaboration with various teams, including IT and business units, to align with governance objectives is essential, along with providing insightful reporting through dashboards.

To qualify for the Data Governance Lead role at Tiger Analytics, candidates should have a minimum of 10 years in data governance or related fields, showcasing a proven experience in leading cross-functional teams. Knowledge of industry-leading data quality practices, data protection management, and familiarity with regulatory requirements is required. Proficiency in data governance tools like Collibra, Informatica, or Alation will also significantly benefit applicants.

Tiger Analytics is dedicated to the career growth of its consultants. As a Data Governance Lead, you will find ample opportunities for professional development in a fast-paced, entrepreneurial environment. You'll work on varied projects across Fortune 500 companies, allowing you to expand your leadership skills and expertise in data governance. The culture promotes collaboration and innovation, providing a supportive backdrop for your career advancement.

A Data Governance Lead at Tiger Analytics should be proficient in tools that facilitate data governance and management. Key tools include Collibra, Informatica, and Alation. Understanding how to utilize these platforms effectively will be vital for implementing governance frameworks, managing metadata, and ensuring data integrity across the organization.

Data quality metrics are essential for the Data Governance Lead role at Tiger Analytics to ensure data accuracy, consistency, and integrity. By establishing and monitoring these metrics, the lead can identify potential data-related issues and discrepancies, allowing the organization to maintain high standards in data usage and reporting, ultimately driving better business decisions.

Certainly! When answering, focus on specific frameworks you've developed, detailing the processes you used and the outcomes achieved. Mention any key policies you implemented and how they improved data quality and compliance within the organization.

Discuss your strategies for defining roles and responsibilities, along with any tools or methods you've used to monitor compliance and hold individuals accountable. Be prepared to share examples of conflicts you've resolved or improvements you've noted.

Identify the specific metrics you've used in past roles, such as data accuracy or consistency rates. Explain how these metrics helped evaluate the organization’s data health and why they are crucial for effective data governance.

Prepare to share a specific scenario where you successfully identified and resolved a major data inconsistency. Detail the steps you took, the team involvement, and what changes improved the situation moving forward.

Talk about the resources, networks, or conferences you join to stay updated with regulatory changes affecting data governance. Mention any frameworks or standards you've applied that align with regulatory requirements.

Discuss your hands-on experience with tools like Collibra, Informatica, or Alation, highlighting how you've utilized them in developing data dictionaries, business glossaries, and managing data integrity across projects.

Provide examples of how you've facilitated collaboration in the past. Describe your communication techniques, conflict resolution skills, and how you ensured alignment between business objectives and technical capabilities.

Identify typical challenges such as resistance to change, insufficient data literacy, or integration issues. Discuss the strategies you've used to overcome these challenges, including training, clear communication, and stakeholder engagement.

Explain the reporting frameworks you’ve established, what KPIs you track, and the tools you use for visualization. Describe how these insights help drive decision-making and awareness across the organization.

Discuss the steps you take to evaluate existing data lifecycle processes and what criteria you use for assessing their effectiveness. Share how you’ve implemented improvements and the results achieved from those changes.