The Future of Data Engineering & AI with Henry Clavo
In this episode of Data Builders Club, Henry Clavo shares lessons from over a decade in data engineering across healthcare and government, exploring what it really takes to build reliable data systems in the age of AI. From ETL best practices and data quality to AI hallucinations, observability, and the future of data engineering careers, this conversation is packed with practical insights for modern data teams.
Data Builders Club celebrates data professionals building reliable, scalable, and trustworthy data systems. If you have real-world lessons and experiences to share, we'd love to hear your story.
Join Data Builders Club: https://www.hevodata.com/data-builders-club/
In this interview, you'll learn:
- Why business ROI matters more than building complex pipelines
- Build vs. buy decisions for ETL tools
- Batch vs. streaming: choosing the right architecture
- Why documentation and governance are critical for long-term success
- How poor data quality leads to AI hallucinations
- The importance of observability in modern data platforms
- Why data engineering is evolving into AI engineering
- The skills data professionals need to stay ahead
Whether you're a data engineer, analytics engineer, AI practitioner, or someone building modern data platforms, this conversation offers practical lessons from real enterprise projects.
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