Data Quality Analyst

Overview

Analyst role responsible for designing, implementing, testing and maintaining automated data quality validations and rules for R&D and Global Medical data, working with Data Stewards and platform teams.

What you'll do

• 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 results with the Data Steward, refining 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, and support regular reviews with business owners and Stewards.
• Use 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.
• Train and mentor new analysts and other team members on 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.

What you'll need

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

Nice to have

• Master's degree in a related field.
• 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.

Details

• Location: Bengaluru, India.
• Worker type: Employee; sub-type: Regular.
• Time type: Full time.