AI for Data Engineers: Faster Pipelines, Less Manual Effort and AI-Ready Data Infrastructure
About This Course
The AI for Data Engineers Faster Pipelines, Less Manual Effort and AI-Ready Data Infrastructure course equips participants with the practical skills to apply Generative AI responsibly throughout the data engineering lifecycle. Learners will use AI tools such as ChatGPT, Google Gemini and GitHub Copilot to accelerate pipeline design, SQL and Python development, testing, documentation, governance and operational support, while ensuring technical accuracy through systematic validation.
The course follows a continuous real-world case study, enabling participants to design, build, test, document, monitor and govern an end-to-end data pipeline using AI-assisted development practices.
What You'll Learn
● LO2 - Translate business and technical requirements into a pipeline design, covering source-to-target mapping, data flow and the batch-versus-streaming decision, and verify that design against source data.
● LO3 - Generate, review and optimise SQL, Python and ETL transformation code using AI, validating output against the target schema and known-correct results.
● LO4 - Design and calibrate data quality checks and test suites covering schema, nulls, duplicates, referential integrity, freshness and reconciliation.
● LO5 - Draft and validate data contracts and detect breaking changes and schema evolution before they affect downstream consumers.
● LO6 - Produce operational artefacts including documentation, lineage metadata, monitoring checklists and runbooks, and apply a governance, security and AI-readiness assessment to a published dataset.
Entry Requirements
• Read, write, and speak English at WPL Level 4
• Manipulate numbers at WPN Level 4
• Fundamental knowledge of SQL queries is expected.
• Basic Python scripting ability, or equivalent hands-on ETL tooling experience
• Familiarity with data warehouse or data pipeline concepts