Data Engineer

Role Overview

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.

What You Will Do

Assess data readiness for priority AI use cases, help identify data sources, define data definitions, quality requirements, and usage constraints, monitor data quality, establish and maintain data management practices, support dataset preparation, validation, and ongoing maintenance, coordinate data access, retention, privacy, and governance requirements.

Why It Might Be a Fit

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.

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.
• 8-12 years of experience in data management, data governance, data quality, business intelligence, analytics support, or related roles, ideally within a corporate environment.
• Proven experience designing and implementing pipelines that convert unstructured and semi-structured data into structured, high-quality datasets for enterprise analytics and AI use cases.
• At least 2 years of hands-on experience with RAG architectures, Vector Databases, embeddings, and LLM-powered solutions, coupled with a strong passion for learning and applying new AI technologies.
• 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.