Specialist, Data Strategy & Governance

The Specialist, Data Strategy & Governance will be responsible for analyzing, monitoring, and improving the quality of enterprise data across data platforms, migration programs, and business processes.

This role will perform advanced data profiling and validation, identify data anomalies and quality gaps, lead the investigation of data defects, and partner with business and technology teams to implement sustainable remediation solutions. The individual will also contribute to the development of enterprise data quality standards, controls, metrics, and reusable practices.

The successful candidate will bring strong analytical and technical capabilities, hands-on experience with enterprise data quality tools, and the ability to work independently across multiple stakeholders and complex data environments.

• Perform in-depth data profiling and analysis to proactively identify data anomalies, inconsistencies, completeness issues, and data quality gaps.
• Translate data analysis findings into actionable recommendations for data cleansing, remediation, enrichment, and preventive controls.
• Define, document, and implement data quality rules, validation checks, thresholds, and monitoring requirements based on business and technical requirements.
• Collaborate with Data Engineers and platform teams to embed automated data quality checks within data ingestion, transformation, and migration pipelines.
• Lead the triage, investigation, root-cause analysis, and resolution of data defects and validation failures.
• Work closely with business stakeholders, IT teams, Data Engineers, Data Stewards, and Project Management teams to resolve complex data quality issues.
• Document data quality issues, remediation actions, root causes, business impacts, and resolution status in a clear and traceable manner.
• Develop comprehensive data quality reports, dashboards, metrics, and insights for business and technology stakeholders.
• Contribute to the development and continuous enhancement of enterprise data quality standards, best practices, methodologies, templates, and reusable artifacts.
• Identify opportunities to automate manual data validation, reconciliation, profiling, and reporting processes to improve efficiency and scalability.
• Support data migration and transformation programs by validating source-to-target data, reconciliation results, and business-critical data attributes.
• Promote consistent adoption of data quality practices across projects, platforms, and data domains.
• Provide technical guidance and knowledge sharing to project teams and less-experienced team members on data quality concepts, tools, and practices.

• Bachelor's degree in Information Systems, Computer Science, Engineering, Data Management, or a related discipline.
• Typically 8-11 years of professional experience in Data Quality, Data Management, Data Engineering, Data Governance, or a related area.
• Hands-on experience performing data profiling, data validation, data quality rule implementation, data remediation, and data quality issue management.
• Experience using enterprise data quality tools such as Collibra Data Quality, Ataccama, Informatica Data Quality, or equivalent platforms.
• Advanced proficiency in SQL, including experience writing and optimizing complex queries for large-scale datasets.
• Experience working with cloud and modern data platforms, including Amazon S3 and Databricks.
• Hands-on experience with Python for data analysis, validation, automation, or data quality rule development.
• Working knowledge of CI/CD practices and orchestration tools such as Apache Airflow.
• Experience performing root-cause analysis and resolving complex data quality defects across integrated data environments.
• Strong analytical and problem-solving skills, with excellent attention to detail and a commitment to delivering high-quality outcomes.
• Ability to work independently, manage competing priorities, and deliver results in a complex, cross-functional environment.
• Strong written, verbal, and presentation skills, with the ability to communicate technical findings clearly and concisely to both technical and business audiences.
• Proficiency in Microsoft Word, Excel, and PowerPoint.

• Experience implementing advanced data quality capabilities such as data observability, anomaly detection, automated data monitoring, or augmented data quality.
• Experience applying artificial intelligence or machine learning capabilities to data profiling, rule recommendation, issue detection, or data quality remediation.
• Strong working knowledge of the Databricks platform, including notebooks, workflows, Delta Lake, or Unity Catalog.
• Experience developing data quality dashboards or reports using Power BI.
• Experience with Collibra Data Catalog, data governance workflows, business glossaries, metadata management, or data lineage.
• Experience supporting large-scale data migration, cloud transformation, or enterprise data modernization initiatives.
• Familiarity with data governance, metadata management, master data management, and FAIR data principles.
• Experience working in the life sciences, pharmaceutical, healthcare, or another highly regulated industry.

• Drives Results: Takes ownership of deliverables and consistently produces accurate, timely, and high-quality outcomes.
• Manages Ambiguity: Effectively analyses incomplete or complex information and develops practical approaches to resolve data challenges.
• Decision Quality: Uses data, analysis, and sound judgement to make well-informed recommendations.
• Collaborates: Builds effective relationships across business, data, engineering, governance, and project teams.
• Communicates Effectively: Presents complex data quality findings in a clear, concise, and stakeholder-appropriate manner.
• Cultivates Innovation: Identifies opportunities to automate processes and apply modern data quality capabilities.
• Ensures Accountability: Maintains clear ownership, documentation, traceability, and follow-through for data quality issues.
• Instils Trust: Demonstrates integrity, transparency, reliability, and attention to detail.

• Improved accuracy, completeness, consistency, and reliability of enterprise data.
• Earlier identification and resolution of data defects within migration and data engineering pipelines.
• Increased automation of data profiling, validation, reconciliation, and monitoring activities.
• Consistent implementation of enterprise data quality standards and reusable controls.
• Clear visibility of data quality risks, root causes, remediation actions, and business impacts.
• Stronger collaboration between business, data governance, engineering, and technology teams.