3 Data Cataloguing Jobs

Job Description: Head of AI, Data & Integration Reports to: Global CIO Role Summary We are seeking a grounded yet visionary leader to serve as Head of AI, Data & Integration. This role will oversee enterprise-wide initiatives in data management, analytics, integration, and AI/ML (including Generative AI), enabling the company to make better decisions, operate more efficiently, and unlock new value through data. It is both a strategic and delivery-focused role, with the clear expectation of influencing our transformation journey without disrupting what already works. The ideal candidate brings a builder” mindset, strong business acumen, technical depth in AI and data platforms, and a collaborative approach to driving change. This role will shape our data and AI agenda while directly managing teams and partners involved in Data Governance, Master Data Management, Enterprise Integration, Business Intelligence, and the scalable deployment of AI/ML solutions. Key Responsibilities Enterprise Data & Integration Strategy Lead the enterprise data architecture, integration platforms (Boomi, SAP CPI, etc.), and data engineering roadmap. Establish data standards, pipelines, and models to support operational, financial, and commercial processes. Ensure interoperability across ERP, supply chain, R&D, manufacturing, and commercial systems. AI & Analytics Leadership Define and drive the AI/ML and GenAI strategy — identifying and delivering high-impact use cases across business functions. Build shared, scalable AI capabilities to support knowledge search, process optimization, and intelligent decision support. Collaborate with cross-functional stakeholders to embed data and AI into day-to-day workflows. Data Governance & Quality Lead master data management (MDM), data governance, and data quality initiatives to ensure reliable and compliant data assets. Implement robust frameworks for data stewardship, access control, auditability, and lifecycle management. Align data practices with enterprise compliance standards (e.g., 21 CFR Part 11, GxP, cybersecurity). Organizational Enablement & Innovation Build and mentor a strong internal team covering data engineering, data science, BI/analytics, and integration development. Partner with external vendors, innovation hubs, and platforms to augment capabilities and stay ahead of emerging trends. Create a culture of data literacy, continuous learning, and practical experimentation. Program Execution & Value Delivery Deliver cross-functional AI/data programs with clarity, speed, and business impact. Establish performance metrics to measure ROI, adoption, data quality, and operational efficiency improvements. Ensure successful scaling of AI and data-driven solutions, from proof-of-concept through to enterprise rollout. Required Experience & Qualifications 20+ years in IT or digital transformation roles, with deep experience in enterprise data, integration, and analytics platforms. 7+ years of direct leadership over Big Data/AI/ML initiatives and cross-functional delivery teams. Experience building and scaling data pipelines, governance models, MDM programs, and integration frameworks in complex enterprises. Proven ability to deliver business outcomes using GenAI, ML, and automation. Strong leadership, communication, and stakeholder management skills across business and technical functions. Industry experience in life sciences, pharmaceuticals, or other regulated sectors preferred.

ABOUT THE ROLE Role Description: We are seeking a highly skilled and experienced hands-on Test Automation Engineering Manager with a deep e xpertise in Data Quality (DQ) , Data Integration (DIF) , and Data Governance . In this role, you will design and implement automated frameworks that ensure data accuracy, metadata consistency , and compliance throughout the data pipeline , leveraging technologies like Data bricks , AWS , and cloud-native tools . You will have a major focus on Data Cataloguing and Governance , ensuring that data assets are well-documented, auditable, and secure across the enterprise. In this role, you will be responsible for the end-to-end design and development of a test automation framework, working collaboratively with the team. As the delivery owner for test automation, your primary focus will be on building and automating comprehensive validation frameworks for data cataloging , data classification, and metadata tracking, while ensuring alignment with internal governance standards. will also work closely with data engineers, product teams, and data governance leads to enforce data quality and governance policies . Your efforts will play a key role in driving data integrity, consistency, and trust across the organization. The role is highly technical and hands-on , with a strong focus on automation, metadata validation , and ensuring data governance practices are seamlessly integrated into development pipelines. Roles & Responsibilities: Data Quality & Integration Frameworks Design and implement Data Quality (DQ) frameworks that validate schema compliance, transformations, completeness, null checks, duplicates, threshold rules, and referential integrity. Build Data Integration Frameworks (DIF) that validate end-to-end data pipelines across ingestion, processing, storage, and consumption layers. Automate data validations in Databricks/Spark pipelines, integrated with AWS services like S3, Glue, Athena, and Lake Formation. Develop modular, reusable validation components using PySpark, SQL, Python, and orchestration via CI/CD pipelines. Data Cataloging & Governance Integrate automated validations with AWS Glue Data Catalog to ensure metadata consistency, schema versioning, and lineage tracking. Implement checks to verify that data assets are properly cataloged, discoverable, and compliant with internal governance standards. Validate and enforce data classification, tagging, and access controls, ensuring alignment with data governance frameworks (e.g., PII/PHI tagging, role-based access policies). Collaborate with governance teams to automate policy enforcement and compliance checks for audit and regulatory needs. Visualization & UI Testing Automate validation of data visualizations in tools like Tableau, Power BI, Looker , or custom React dashboards. Ensure charts, KPIs, filters, and dynamic views correctly reflect backend data using UI automation (Selenium with Python) and backend validation logic. Conduct API testing (via Postman or Python test suites) to ensure accurate data delivery to visualization layers. Technical Skills and Tools Hands-on experience with data automation tools like Databricks and AWS is essential, as the manager will be instrumental in building and managing data pipelines. Leverage automated testing frameworks and containerization tools to streamline processes and improve efficiency. Experience in UI and API functional validation using tools such as Selenium with Python and Postman, ensuring comprehensive testing coverage. Technical Leadership, Strategy & Team Collaboration Define and drive the overall QA and testing strategy for UI and search-related components with a focus on scalability, reliability, and performance, while establishing alerting and reporting mechanisms for test failures, data anomalies, and governance violations. Contribute to system architecture and design discussions , bringing a strong quality and testability lens early into the development lifecycle. Lead test automation initiatives by implementing best practices and scalable frameworks, embedding test suites into CI/CD pipelines to enable automated, continuous validation of data workflows, catalog changes, and visualization updates Mentor and guide QA engineers , fostering a collaborative, growth-oriented culture focused on continuous learning and technical excellence. Collaborate cross-functionally with product managers, developers, and DevOps to align quality efforts with business goals and release timelines. Conduct code reviews, test plan reviews, and pair-testing sessions to ensure team-level consistency and high-quality standards. Good-to-Have Skills: Experience with data governance tools such as Apache Atlas , Collibra , or Alation Understanding of DataOps methodologies and practices Familiarity with monitoring/observability tools such as Datadog , Prometheus , or CloudWatch Experience building or maintaining test data generators Contributions to internal quality dashboards or data observability systems Awareness of metadata-driven testing approaches and lineage-based validations Experience working with agile Testing methodologies such as Scaled Agile. Familiarity with automated testing frameworks like Selenium, JUnit, TestNG, or PyTest. Must-Have Skills: Strong hands-on experience with Data Quality (DQ) framework design and automation Expertise in PySpark, Python, and SQL for data validations Solid understanding of ETL/ELT pipeline testing in Databricks or Apache Spark environments Experience validating structured and semi-structured data formats (e.g., Parquet, JSON, Avro) Deep familiarity with AWS data services: S3, Glue, Athena, Lake Formation, Data Catalog Integration of test automation with AWS Glue Data Catalog or similar catalog tools UI automation using Selenium with Python for dashboard and web interface validation API testing using Postman, Python, or custom API test scripts Hands-on testing of BI tools such as Tableau, Power BI, Looker, or custom visualization layers CI/CD test integration with tools like Jenkins, GitHub Actions, or GitLab CI Familiarity with containerized environments (e.g., Docker, AWS ECS/EKS) Knowledge of data classification, access control validation, and PII/PHI tagging Understanding of data governance standards (e.g., GDPR, HIPAA, CCPA) Understanding Data Structures: Knowledge of various data structures and their applications. Ability to analyze data and identify inconsistencies. Proven hands-on experience in test automation and data automation using Databricks and AWS. Strong knowledge of Data Integrity Frameworks (DIF) and Data Quality (DQ) principles. Familiarity with automated testing frameworks like Selenium, JUnit, TestNG, or PyTest. Strong understanding of data transformation techniques and logic. Education and Professional Certifications Bachelors degree in computer science and engineering preferred, other Engineering field is considered; Masters degree and 6+ years experience Or Bachelors degree and 8+ years Soft Skills: Excellent analytical and troubleshooting skills. Strong verbal and written communication skills Ability to work effectively with global, virtual teams High degree of initiative and self-motivation. Ability to manage multiple priorities successfully. Team-oriented, with a focus on achieving team goals Strong presentation and public speaking skills.

Candidate with 5–8 years of overall experience in software engineering, including 2-4 years of focused expertise in Data Governance practices such as data cataloguing, data lineage, data quality, data purging, data reliability, data accessibility. Required Candidate profile Strong experience with data governance tools (Ab Initio, Informatica, Atlan, Collibra, Snowflake,Databricks) Hands-on knowledge of data cataloguing, data lineage, data quality, and data access control Perks and benefits To be disclosed post interview