Data Engineer

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.

Essential Requirements:

• Bachelor’s degree in Information Systems, Computer Science, Data Management, Business Analytics, Statistics, Engineering, or a related field.
• Additional training or certification in data management, data governance, data quality, or analytics is preferred.
• 4-7 years of experience in data management, data governance, data quality, business intelligence, analytics support, or related roles, ideally within a corporate environment.
• Extensive experience with AI platforms or toolchains (Claude, GPT, Azure OpenAI, Langfuse, vector databases etc.)
• Experience assessing data readiness, documenting data sources and definitions, and supporting data quality improvement for business-critical processes, analytics, or technology solutions.
• Strong understanding of data quality dimensions, metadata, lineage, ownership, controls, and the data management practices required to support reliable business outcomes.
• Experience partnering with IT, data owners, and business stakeholders to improve data consistency, accessibility, and governance across multiple workstreams.
• Familiarity with the data requirements that underpin AI, machine learning, or advanced analytics solutions, including the importance of fit-for-purpose, well-governed, and trusted data inputs.
• Demonstrated track record of identifying data issues, coordinating remediation, and improving confidence in the data supporting reporting, analytics, or digital solutions.
• Experience producing clear documentation, status reporting, and management information on data quality, risks, and remediation progress.
• Understanding of data privacy, access controls, retention requirements, and responsible data handling practices in a corporate setting.

Desirable Requirements:

• Experience supporting Corporate Affairs, communications, reputation, or other business-facing functions is preferred.