Data Engineer

Location: Hyderabad (Office)

Job Description Summary

This role is an opportunity to build the data foundations that make AI useful, trusted, and scalable within Corporate Affairs. The Data Management Analyst will support the quality, structure, accessibility, and governance of data used across the AI portfolio, helping ensure that solutions are powered by data that is accurate, compliant, and fit for purpose. Working closely with the Director, AI Enablement and the Corporate Affairs AI Enablement team, this role will strengthen the data discipline required for AI solutions to perform effectively in production.

In this role you will be accountable for improving data readiness and supporting data governance across active AI use cases. Success in the role will be measured by the ability to increase confidence in data quality and enable reliable AI outputs through strong standards, controls, and day-to-day data management practices.

Key Responsibilities

Assess data readiness for priority AI use cases, helping identify whether the required data is available, usable, and of sufficient quality to support reliable solution performance.

Partner with data owners, IT, and delivery teams to define and document data sources, data definitions, quality requirements, and usage constraints for each initiative.

Monitor data quality against agreed standards and highlight issues early, supporting remediation actions that improve the accuracy and dependability of AI outputs.

Help establish and maintain data management practices such as metadata, lineage, documentation, and control processes that improve transparency and reusability across the portfolio.

Support the preparation, validation, and ongoing maintenance of datasets used in AI solutions, ensuring they remain current, structured, and fit for business use.

Coordinate with relevant teams on data access, retention, privacy, and governance requirements so AI use cases are supported by compliant and well-controlled data practices.

Identify recurring data issues and improvement opportunities across the portfolio, contributing to stronger long-term data foundations rather than one-off fixes.

Produce clear reporting on data quality status, readiness risks, and remediation progress, giving stakeholders better visibility into the health of the data supporting AI delivery.

Contribute to responsible AI governance by helping ensure data used in AI solutions is handled appropriately, documented clearly, and aligned with internal policy and regulatory expectations.