Systems | Development | Analytics | API | Testing

The Art of Building Reliable Data Stack with Sergio Ramos

In this episode of Data Builders Club, Sergio shares how teaching himself Excel sparked a career in analytics, why business context matters more than building flashy dashboards, and what it really takes to build reliable data systems that stakeholders trust. We also dive into data governance, stakeholder communication, AI in modern data teams, and why first-principles thinking will matter even more in the age of AI.

Why Salesforce ETL Tools Keep Breaking (And What Actually Fixes It)

Salesforce integration always starts simple. You pull a few objects, set up incremental syncs, push the data into your warehouse. Then Salesforce changes. Objects shift, fields get added, data types evolve, APIs update, and rate limits get hit and your simple connector turns into a full-time maintenance job.

Accelerate eCommerce with Marketing Intelligence on Snowflake

Marketing data is scattered across ad platforms, CRMs, eCommerce tools, and analytics solutions. Without a unified view, measuring campaign performance, customer acquisition, retention, and revenue becomes slow and unreliable. In this webinar, PROLIM and Hevo demonstrate how modern eCommerce and D2C companies build a centralized marketing intelligence platform on Snowflake. Learn how to automate data ingestion with Hevo, create a single source of truth, and build executive-ready Streamlit dashboards that help teams make faster, data-driven decisions.

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.

MAR-Pricing trap your data teams do not know about

Most data teams think MAR pricing is predictable and manageable. You pay for rows that change, your pipelines keep running, and the monthly bill should be easy to understand. But teams on the pricing model learn the reality 6 months in. If you’re on the MAR model this is a must watch (before you get your next bill spike). Dan Murphy explains why MAR mechanics are designed to break.