Role Purpose​

Enable decision-ready insights by maximizing enterprise-data usability and accessibility — leveraging AI tools to accelerate data analysis, pattern detection, documentation, and insight generation while keeping human accountability for interpretation and business decisions.​

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Core Responsibilities​

• Design and develop data pipelines and perform ETL/ELT jobs based on functional/non-functional business requirements
• Plan and implement data extraction, integration, and ingestion procedures to unify data from diverse sources and build analyst-ready datasets
• Collaborate with analysts and other business stakeholders to align on requirements, definitions, and ownership
• Translate business logic into structured, testable, and well-documented datasets used across teams
• Maintain structured technical documentation and artifacts to ensure consistency and reuse​
• Drive process improvement and delivery optimization by enhancing data workflows and leveraging cost-effective technologies including API integration, automation, and AI
• Adhere to established data, AI usage, and governance standards​

 

Profile​

• Bachelor’s degree in Business, Statistics, Mathematics, Finance, Data Science, Computer Science, Computer Engineering, or a related field.

• 1–3 years of experience on data management
• Strong analytical reasoning and problem-framing skills​
• Ability to process, manage, and interpret complex data from multiple sources and communicate insights effectively​
• Demonstrated comfort using AI/automation tools and languages applicable​ (SQL, Python/R)
• Curiosity and discipline in adopting AI-enabled ways of working responsibly​

• Ability to quickly learn and apply enterprise AI tools and technologies to support technical workflows and business objectives

AI Expectations (Required) ​

Uses AI tools daily to accelerate execution, not to replace judgment. Follows defined AI usage and governance, with increasing independence as experience grows. ​

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• Leverage AI to improve output (speed of delivery and quality) ​
• Embed AI usage in the entire Data Value Chain (Collection-Publication-Uptake-Impact) whenever possible ​
• Assist with data preparation, transformation, and consolidation ​
• Aid with coding/scripting/automation ​
• Draft documentation, reporting narratives, and presentation materials ​
• Evaluate AI output critically for accuracy, relevance, bias, and risks ​
• Use in line with governance and ethical principles​​

 

AI Capability Level​

AI-Fluent User. Uses AI daily to accelerate data usability and accessibility for analysis and reporting purposes, while retaining responsibility for validation, accuracy, and execution. May or may not design AI workflows but applies AI effectively.​

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Success Measures​

• Quality (clarity, reliability, and usefulness) of data assets that enable analysts to deliver actionable insights/recommendations to stakeholders
• Improved speed-to-insight and reduced manual effort and rework​
• Demonstrated growth in analytical judgment and AI proficiency​

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