AI in Data Engineering: Building Reliable Data Systems at Scale
· 13 min read
AI is transforming data engineering. Coding agents can now generate SQL transformations, configure connectors, and define API schemas in minutes rather than days. But here's the catch: a query that works perfectly on test data may fail catastrophically when confronted with late-arriving events, schema evolution, or terabyte-scale volumes.
How do we ensure that AI-generated data systems meet the rigorous non-functional requirements that production data platforms demand? This article presents our framework for integrating AI coding agents into data engineering workflows while maintaining data quality, reliability, governance, and trust.