RN Krishnan

His foundation was built in test data management, functional testing and ETL testing, supported by Python automation and Agile delivery practice.

He developed working command of the test life cycle and the defect life cycle, and a track record of discovering errors, resolving them, and making sure what reached the client met the quality expected. Working with minimum supervision across several concurrent workstreams, he collaborated with developers and project managers to hit delivery milestones.

This is where the discipline underneath his engineering work comes from. Test data management in particular is an unglamorous specialism that teaches something no analytics course does: how to construct data that exercises the edge cases a system will eventually meet in production.

His work in this period was recognised with a best-debutant award, alongside earlier recognition for academic excellence and for a research-based learning project.

He moved from assuring data to interpreting it, taking a data analyst role that bridged his quality background and the engineering work that followed.

It is a short stage on paper and an important one in practice: it is the point at which the question changed from "is this data correct?" to "what does this data mean, and who needs it?"

In his current role he works as a data engineer on Google Cloud Platform, providing data solutions for a range of business requirements.

The combination he brings to it is uncommon. Most data engineers arrive from software or analytics; he arrived from test data management, which means he treats a pipeline the way a test engineer treats a build — as something to be proven rather than assumed, with the failure modes considered before the happy path is celebrated.

Alongside the platform work he has continued into artificial intelligence and generative AI, graph databases, and open-source data science tooling, holding certification as a Google Cloud Professional Data Engineer and as a Neo4j Certified Professional.