Modern data platforms rely on complex pipelines, frequent schema changes, and constant pressure to deliver reliable results faster. Yet many teams still perform critical validation, reporting, and release tasks manually — slowing delivery and increasing the risk of human error.
In this session, Aleksandr shares how he transformed a traditional testing workflow into a fully automated, self-maintaining ecosystem powered by GitHub Copilot and GitHub Actions.
You’ll see how AI-assisted engineering can automate not only validation, but also reporting, documentation, release orchestration, API/DB integrations, and even communication flows across Jira, Confluence, and Teams.
This talk combines real engineering scenarios, architecture diagrams, automation patterns, and a touch of humour — demonstrating how two specialists now handle the workload that previously required two full QA teams. At the same time, former QA engineers successfully transitioned into development roles.
If you’re looking to scale your data quality processes, reduce manual work, and unlock the full potential of AI-driven automation, this session will give you a practical roadmap.
- 1. AI + CI/CD can automate the entire data-quality lifecycle Not just tests — but dataset prep, schema drift detection, validation, reporting, documentation, and release workflows.
- 2. Copilot acts as a force multiplier for engineering teams It accelerates code generation, validation scripts, YAML pipelines, and even Jira/Confluence updates — enabling two specialists to support what used to require two full QA teams.
- 3. Automation unlocks team transformation and higher-value work By removing repetitive tasks, QA engineers can transition into development and platform engineering roles, increasing delivery speed, quality, and innovation across the organisation.
