Data Quality Analyst

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Job Description

• Work with Data Stewards across R&D and Global Medical to profile data, clarify requirements and document proposed quality rules, thresholds and severity for business approval. 

• Develop, configure and maintain automated validations using SQL, Python and the approved data quality tools. Search the shared rule registry first and reuse or adapt approved rules where appropriate. 

• Implement field-level, cross-system and business-process validations, as well as expected-versus-actual load and freshness validations. Ensure each validation produces dependable measurements (records evaluated, passed and failed, or expected and actual loads) and that failed records are easy for the business to understand. 

• Test rules with pass/fail scenarios, then review the results with the Data Steward, refining the logic until the Data Steward confirms the results reflect the business intent. Prepare test evidence and obtain the required business approval before the platform team releases a rule; a rule is complete when it is approved and launched into production.  

• Monitor data quality results over time, maintain scorecards and reporting, then support regular reviews with business owners and Stewards. Use these reviews to identify recurring data issues and recommend tuning rules, adjusting thresholds, or retiring rules that no longer add value. 

• Support data remediation by investigating validation rule and monitoring failures. Help Data Stewards decide whether the cause is the rule, the source data or the data flow; route issues to the responsible team; and confirm fixes through re-testing. 

• Manage new rules, changes, enhancements and retirements through an agreed intake process and work queue. Maintain specifications, test evidence, approval references and change history, raise blockers early and coordinate production handoffs with DD&T and the platform teams. 

• Maintain the team's data quality playbook, including reusable rule patterns, templates, and testing and documentation standards, so new datasets, systems and business groups are onboarded the same way each time. 

• Train and mentor new analysts and other team members on the tools, standards and ways of working, and review their rules and test evidence. Help Data Stewards learn how to request rules, read the results and act on them. 

• Contribute to the evaluation and adoption of data quality tools, platforms and methods, including automation and AI-assisted rule development, and help move rules and evidence when tools or platforms change. 

Accountability and Decision Rights 

The Data Quality Analyst is responsible for the quality of the technical rules, tests, documentation and results within their assigned work and, when reviewing the work of others, for applying the team's standards including solutioning and implementation of validations within the pipeline. This requires an understanding of the architecture and technology stack within Takeda. Data Stewards and Trustees retain responsibility for business definitions, thresholds, risk decisions and approvals. DD&T and the designated platform teams own production deployment and platform operations; source and technical owners remain responsible for correcting defects in their systems. The R&D Data Governance lead sets priorities and resolves escalations. 

 

Education 

• Minimum: Bachelor’s degree in Computer Science, Information Systems, Data Science, Statistics or a related field. 

• Preferred: Master’s degree in a related field. 

Experience 

• Minimum: 8+ years in data quality, data engineering or technical data analysis. 

• Hands-on experience with at least one enterprise data quality, data integration or business rules tool (such as Informatica), and the ability to learn new tools as the platform changes. 

• Experience in data profiling, reconciliation, automated validation, testing and investigation of data quality issues. 

Skills 

Required 

• Solid understanding of data quality dimensions (such as completeness, accuracy, validity, consistency and timeliness) and how to measure them. 

• Strong SQL and working knowledge of Python or a comparable scripting language. 

• Ability to translate business requirements into technical rules and explain methods, standards and findings clearly to nontechnical stakeholders and new team members. 

• Sound troubleshooting, documentation, collaboration and work-prioritization skills. 

• Willingness to coach others, share knowledge and review colleagues' work constructively. 

Desirable 

• Experience with cloud data platforms (such as Databricks and Spark), work-management tools (such as ServiceNow) and reporting tools (such as Power BI). 

• Experience with pharmaceutical, clinical or GxP-related data and applicable quality requirements. 

• Experience training, mentoring or leading the work of other analysts. 

• Familiarity with automation or AI-assisted approaches to data quality. 

 

Locations

IND - Bengaluru

Worker Type

Employee

Worker Sub-Type

Regular

Time Type

Full time