Specialist, Data Quality Stewardship

MSD Animal Health Technology Labs; Hyderabad; On-site; INR 900,000 - 1,500,000; Full time

Job summary

MSD Animal Health Technology Labs in Hyderabad seeks a Specialist, Data Quality Stewardship to join the EDQ&DSC team. This global role ensures data quality across domains, supports business outcomes, and impacts patients relying on life-saving products.

You will onboard data to the enterprise platform, develop quality rules, define KPIs, monitor issues, drive root-cause analysis, and coordinate remediation with stakeholders while pursuing automation and improvements in data governance.

Qualifications

• Proven experience in data quality stewardship and governance.
• Familiarity with enterprise data quality platforms and tooling.
• Ability to translate business requirements into data quality rules.

Responsibilities

• Onboard divisional data to the enterprise data quality platform.
• Develop and maintain data quality rules and checks.
• Define metrics, KPIs, and dashboards for data quality across domains.

Skills

Data quality

Data governance

Root-cause analysis

Automation opportunities

Tools

Data quality platform

Job description

The Opportunity

Based in Hyderabad, join a global healthcare biopharma company and be part of a 130- year legacy of success backed by ethical integrity, forward momentum, and an inspiring mission to achieve new milestones in global healthcare.Be part of an organisation driven by digital technology and data-backed approaches that support a diversified portfolio of prescription medicines, vaccines, and animal health products.Drive innovation and execution excellence. Be a part of a team with passion for using data, analytics, and insights to drive decision-making, and which creates custom software, allowing us to tackle some of the world's greatest health threats.Our Technology Centers focus on creating a space where teams can come together to deliver business solutions that save and improve lives. An integral part of our companys IT operating model, Tech Centers are globally distributed locations where each IT division has employees to enable our digital transformation journey and drive business outcomes. These locations, in addition to the other sites, are essential to supporting our business and strategy.

A focused group of leaders in each Tech Center helps to ensure we can manage and improve each location, from investing in growth, success, and well-being of our people, to making sure colleagues from each IT division feel a sense of belonging to managing critical emergencies. And together, we must leverage the strength of our team to collaborate globally to optimize connections and share best practices across the Tech Centers.

Role Overview

The Specialist, Data Quality Stewardship is part of the EDQ&DSC (Enterprise Data Quality and Data Stewardship) team within the CDAO (Chief Data and Analytics Office). This role plays a pivotal part in ensuring the data quality across multiple domains and divisions, directly supporting business outcomes and ultimately impacting patients who rely on life-saving products. This is a global role supporting data domains and divisions across the pharma value chain. The Specialist works closely with business, data owners, data enablement teams, and IT stakeholders to define data quality standards, monitor data issues, drive root-cause analysis, and support remediation activities. The role also identifies opportunities for automation and innovation to improve data quality monitoring, controls, and issue resolution processes.

What will you do in this role

Data Quality Platform Onboarding, Adoption and Enablement

Work with divisional partners to onboard their data to the enterprise data quality platform. Support adoption of the platform within business teams and promote consistent usage. Understand divisional requirements and configure and implement them within the data quality platform. Serve as a liaison between business data owners, IT, and the CDAO to ensure alignment on data quality priorities. Train users on the data quality platform and encourage consistent, effective usage. Contribute to the development and documentation of platform standards, operating procedures, and best practices. Maintain accurate and high-quality documentation of processes, rules, and controls.

Data Quality Rule Development and Management

Create, maintain, and enhance data quality rules and checks. Develop and execute data quality routines, validations, and monitoring controls. Partner within CDAO teams to align data quality rules with enterprise data policies and standards.

Data Quality Metrics, Reporting & KPIs

Define, measure, and report on data quality KPIs (e.g., completeness, accuracy, timeliness, consistency etc.) across domains. Develop and maintain data quality dashboards and scorecards for leadership visibility. Track trends and provide insights to drive prioritization of data quality improvement initiatives.

Data Quality Monitoring and Issue Management

Review data quality reports and identify issues, trends, anomalies and opportunities for improvement. Investigate data quality issues, perform root-cause analysis, and coordinate remediation with relevant stakeholders. Communicate findings clearly with divisional stakeholders and support remediation efforts. Facilitate data quality issue resolution discussions with cross-functional stakeholders. Maintain a data quality issue log, track resolution progress, and ensure timely closure. Define and follow escalation paths for critical data quality issues impacting downstream processes. Support adherence to SLAs for data quality issue resolution. Ensure data quality practices align with regulatory requirements and documentation (e.g., GxP, FDA, EMA) where applicable. Support audit readiness by maintaining traceability of data quality rules, controls, and remediation actions.

Automation, Innovation and Continuous Improvement

Contribute innovative ideas to improve data quality management processes, controls, and user experience. Develop automation utilities to improve monitoring, reporting, and operational efficiency. Apply analytical .