When I joined Deepnote as a software engineer, I quickly realized I knew nothing about data tools despite thinking data science was adjacent to software engineering. After months of research and questioning colleagues, I created this guide for developers who feel lost in data team conversations.

The article outlines four data profession types: Analytical (SQL, BI tools like Tableau, dashboards), Scientific (Python, notebooks, statistical modeling), Engineering (pipelines, Spark, warehouses, infrastructure), and Machine Learning (model training, deployment, monitoring). Each uses distinct toolsets despite overlapping responsibilities.

The core data lifecycle follows ETL (Extract-Transform-Load) or ELT (Extract-Load-Transform) patterns. Data moves from sources through processing into warehouses or lakes where analytical and scientific work happens. Engineering types build and maintain this infrastructure so others can focus on insights.

This guide won't teach dashboard creation or cluster administration. Instead, it maps tools to lifecycle stages so developers understand which problems each tool solves and how they fit together.