Data Engineer – AI Value & DX Platform

Project Overview

The project focuses on the implementation of the DX platform (getdx.com) as a Proof of Concept (PoC), with a strong focus on objectively measuring the value generated by AI-assisted software development tools.

AI-powered development tools such as GitHub Copilot, coding agents, and AI-assisted code review solutions are increasingly being adopted. However, their actual business value is currently difficult to measure reliably.

The objective of the PoC is to assess whether AI adoption, usage intensity, development time savings, and the impact on quality and lead times can be reliably measured, integrated into existing systems, and used to support well-founded ROI, investment, and scaling decisions.

Key Responsibilities

Support the selection of pilot teams and the definition of an AI value measurement model.
Define measurement and success criteria covering AI adoption, usage intensity, time savings, lead time, quality impact, and ROI.
Set up and configure the DX platform, including SSO/identity integration and the permission model.
Integrate DX with existing GitLab, CI/CD, ticketing, and identity/SSO systems.
Integrate usage and telemetry data from the AI tools in use, including GitHub Copilot usage data / Copilot Metrics API.
Configure teams, hierarchies, and data models within the DX platform.
Set up and validate identity and team mapping across AI tools and delivery systems.
Link AI-related metrics with relevant cost data.
Set up the DX AI Measurement Framework surveys and system-side data collection.
Establish baseline measurements before and after the expansion of AI usage.
Analyze the impact of AI on quality-related metrics such as change failure rate, review effort, rework, and security findings, ensuring that AI value is not assessed solely in terms of speed.
Ensure data quality, consistency, and completeness, including error analysis and correction.
Implement technical requirements related to anonymization, aggregation, and data minimization.
Ensure a clear separation between AI usage measurement and individual performance measurement.
Develop dashboards, reports, and exports to support results analysis and ROI assessment.
Identify the most effective AI use cases and key barriers to adoption based on the evaluation results.
Support the definition of enablement measures and the future scaling and license strategy.
Prepare technical documentation and estimate the effort and resources required for future scaling.

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Originally posted on Himalayas