All Writing
Essays and practical guides on data engineering, product analytics, and the frameworks that connect raw data to business decisions.
Practical SQL examples for common data transformation tasks in Athena/Presto syntax, with inline comments explaining datatypes and outputs.
Read more →The finance team's churn number is 4.2%. The product team's churn number is 6.8%. Same company. Same quarter. The disagreement traces back to the foundation.
Read more →GTM brought in the account. Product built the features. Engineering kept the platform running. And yet the customer churned. This is a measurement problem.
Read more →Revenue is a lagging indicator. By the time it moves, the decision window has already closed. These are the metrics that tell you what is coming first.
Read more →Most of the roles we now consider standard in the data ecosystem didn't exist in the late 2000s. A first-hand account of how data specialization evolved.
Read more →Customer lifetime value is one of the most cited and least correctly calculated metrics in SaaS. Here is the math that actually matters.
Read more →Most ML projects fail before the model is built. The problem is rarely the algorithm — it is the data, the problem definition, and the organizational readiness.
Read more →Practice exercises for SQL fundamentals with real-world marketing and product scenarios.
Read more →Intermediate SQL for digital marketers — CASE statements for conditional logic, GROUP BY for aggregation, and HAVING for filtering grouped results.
Read more →SQL fundamentals for digital marketers — filtering rows, aggregating data, and joining tables with practical examples.
Read more →What is a database? What is a table? What is a query? The foundations of data literacy for non-technical professionals.
Read more →What to study, what to build, and what to expect before entering a formal data science program.
Read more →From problem definition to production deployment — the complete lifecycle of an ML project and where most teams get it wrong.
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