All Writing
Essays and practical guides on data engineering, product analytics, and the frameworks that connect raw data to business decisions.
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 →Practical SQL examples for common data transformation tasks in Athena/Presto syntax, with inline comments explaining datatypes and outputs.
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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