Emily Riederer: Column selectors, data quality, and learning in public

Emily Riederer writes Python with an R accent, and we’re all comfortable with it. In this episode, Emily reflects on her journey through R, Python, and SQL — from lessons learned in averaging default values (oops, we're not all rich!) to discovering that column selectors are way cooler than they sound. She weighs in on the delicate art of learning in public, why frustration often makes the best teacher, and how to find your niche by solving the boring problems. Oh, Oh, and the crew casually drops that she's keynoting posit::conf 2026!

Episode Notes
Emily’s had a wild ride through modeling, data engineering, machine learning, and back again, and she knows a thing or three about the evolution of SQL tooling (from nightmare multi-page scripts to the dbt renaissance). She reveals how building internal packages became her gateway to making work enjoyable. Plus: the surprising Stata origins of column selectors, the eternal struggle of naming packages across R and Python, and why watching people code teaches you more than any tutorial ever could. The conversation gets real about imposter syndrome and the magic of tacit knowledge.

What’s Inside

• Why real-world data is chaos, not truthThe path from modeling to data engineering (and back)
• What a data pipeline really is (extract, load, transform) and why organization matters
• How dbt changed the SQL game Learning by watching: Tacit knowledge and coding over the shoulder Imposter syndrome and learning in public 
• Building internal tools to escape busyworkposit::conf 2025 keynote preview