Data Engineering Graduate Trainee Program

By the end of the journey every fresher will have moved from “zero-to-production” confidence in data engineering. The program is designed to turn raw graduates into team-ready junior data engineers who can read requirements, model data, write efficient SQL and PySpark, and automate robust batch-and-stream pipelines on the Databricks + Azure stack. In the process they will internalise modern engineering disciplines—testing, packaging, DevOps, CI/CD, data governance and observability—so that their code ships safely and can be trusted by the business.

After completing the program a learner can fluently explain relational, dimensional and Data Vault modelling choices and implement each one in Spark and Delta Lake; author indexed CRUD, analytic and CDC SQL that meets defined SLAs; build, orchestrate and monitor scalable pipelines in Databricks, Delta Live Tables, Apache NiFi and Azure Data Factory; enforce quality rules with Deequ and data expectations; secure and catalog data with Unity Catalog and Purview; package Python projects with tests, publish them to notebooks and jobs, and promote them through GitHub-Actions-based CI/CD all the way to production. Each graduate will have demonstrated these skills in a capstone project reviewed by peers and presented to engineering leadership.