Enterprise Data Quality Engineering

Building Trusted Data at Scale — A Practical Engineering Guide to Profiling, Cleansing, Validation, Monitoring, and DataOps for Trusted Enterprise Data

Data quality strategy describes intent. Data quality engineering is the code, pipelines, and rule engines that actually enforce it — continuously, at scale, in production.

This is the hands-on engineering companion in the series: profiling pipelines, cleansing and entity resolution, validation rule engines embedded in ETL and AI workflows, and continuous monitoring under a DataOps operating model.

Inside the book

Part I — Foundations: the data quality engineering stack and quality as code
Part II — Profiling: statistical techniques, anomaly detection, profiling at scale
Part III — Cleansing: pipelines, deduplication, entity resolution, standardization
Part IV — Validation rule engines in ETL, APIs and AI workflows, with versioning
Part V — Continuous monitoring architecture and DataOps
Practical patterns for embedding quality controls in production data platforms

Written for

Data engineersData platform and DataOps teamsAnalytics engineering leadsData quality practitioners