• Planning, leading, and executing testing efforts specific to CDAO’s data cataloging and data governance work, including incorporation of AI into data curation. • The candidate will design and conduct test cases to validate the accuracy, completeness, and consistency of updates; oversee end-to-end testing activities; improve automation and streamlined updates to existing technology and solutions; and ensure efficient identification and remediation of issues. • The ideal candidate will act as a subject matter expert on data governance and cataloging, addressing related quality concerns and ensuring prompt, effective resolution. • Develop and refine processes that ensure the effectiveness of data cataloging and quality assurance, support re-engineering and change management initiatives, and oversee ongoing process improvements through detailed root cause analysis and the application of corrective measures. • The candidate will also be expected to provide support for development of testing and confirming of CDAO role-based access controls for Databricks data and have an understanding of the principles of RBAC, ABAC, and the principles of least privilege • The candidate will also be expected to provide support, insight, and ideas for improvements to existing data governance, data cataloging, Data Lakehouse, and master data efforts • Day to day tasks may include: Design, write, and execute test scripts for data cataloging releases, user interfacing platforms, or implementation of role-based access controls. • Lead and participate in testing efforts to ensure data assets meet CDAO standards for accuracy and completeness. • Support the implementation and continuous improvement of Data Governance Architecture. • Participate in regular meetings with program managers, developers, and leadership. • Identify the root cause of issues identified with the data

• Work closely with Product, UX, Customer Success, and Security engineers to establish user acceptance criteria for new external and internal features. • Develop and automate test cases for functional testing to ensure the software meets quality standards. • Build and maintain dependable test automation using Playwright. • Provide guidance to the Engineering and Product teams on implementing effective test strategies to maintain high-quality standards. • Develop and maintain test data and test environments.

• Design and execute test plans for A.I and Machine Learning driven features including classification, summarization, anomaly detection, and autofill systems • Develop Python-based automation and evaluation scripts to validate model performance, reliability, and output quality • Test LLM-based features for accuracy, guardrail effectiveness, hallucination risk, and behavior under edge cases • Validate data pipelines, input/output transformations, and model integration points across Keeper’s platform • Conduct regression testing on ML models and AI workflows to ensure reliability across releases • Test enterprise-scale behavior analysis features driven by data from ARAM, PAM, and session recordings • Validate security, privacy, and zero-trust requirements for AI model interactions and data handling • Collaborate with ML engineers and cross-functional teams to identify defects and improve model behavior • Analyze logs, metrics, and model outputs to detect anomalies, performance issues, or silent failures

• Validate database functionality, data structures, and backend data flows across the Keeper platform and encrypted vault • Test CRUD operations, schema updates, migrations, indexing behavior, and cross-service data interactions • Verify data integrity, consistency, and correctness across distributed systems and cloud environments • Execute SQL queries to validate stored data, relationships, constraints, and backend processing logic • Review and test schema changes, stored procedures, triggers, and database-level logic from a QA perspective • Analyze logs, query results, and backend output to identify defects or anomalies • Conduct regression testing related to database updates, schema changes, and backend releases • Validate database performance characteristics and query responsiveness in collaboration with engineering teams • Evaluate vault-level workflows that impact stored records, metadata, and encryption-related data handling • Document defects, test results, and reproducible steps clearly in Jira and related tools • Participate in reviews of data-handling requirements, edge cases, and system behaviors