TL;DR: I’m an analytics engineer with about 3 years experience in data and with a mechanical engineering degree. My skills are spread across data engineering, app dev, and a little data science, without having deep knowledge in any of them. I want to know what’s actually worth learning now, how deep to go, and whether I should specialize. How I got here I studied mechanical engineering (aerospace emphasis), but I was always the tech kid: Java Minecraft mods, jailbroken iPhones, a few websites for local businesses. After some unenjoyable manufacturing engineering internships, I took a Python data analytics course and found a role that combined both worlds. That first job was smart manufacturing at a tool company. I collected machine sensor data (Kepware, OSI PI, SQL Server) and tried ML models to predict machine damage and part quality. Those never reached production because the data was too noisy and failures were too rare. What did work was real-time dashboards for shop floor KPIs like OEE and run rate. Eventually I got tired of crawling around machines debugging PLCs, so I moved to the data side full time. What I do now I’m an analytics engineer on our supply chain team. I pull data from upstream warehouses and model it into star schemas that feed Power BI reports, Databricks dashboards, and Genie rooms. Most of the work is SQL. The business logic is full of exceptions, so some transformations run over 1,000 lines. Pipelines run in Databricks and ADF, and code goes through Azure DevOps (GitHub for apps) CI/CD pipelines. My team is now building internal apps with React, Express, TypeScript, and Databricks AppKit. One experienced developer set up the framework, and the rest of us are mostly vibe coding on top of it. On the side, I’ve taken full-stack courses and built two web games: a multiplayer name guessing game and a daily game where users solve color nonograms. Where I stand Strong: SQL, and Databricks as a platform. I build and deploy pipelines, dashboards, and Genie rooms with Databricks Asset Bundles. Comfortable: dimensional modeling, Power BI, building robust data pipelines, and front-end work in React, Next.js, and Tailwind. Weak: TypeScript, backend development, and infrastructure. (I know what good CI/CD, permissioning, and Azure–Databricks integration look like, but I’ve never set them up myself.) My team’s standards are fairly loose, so I haven’t seen rigorous data engineering practices up close. (Data quality checks, truly idempotent pipelines, etc.) My Python has also slipped, since most of my code is now SQL or AI-assisted. I’m working through Full Stack Open, mainly for TypeScript and CI/CD. I’ve read Fundamentals of Data Engineering and The Pragmatic Programmer . What I’d love your take on How would you sort skills? If you had to put skills into “learn deeply,” “learn enough to read and review,” or “skip,” what goes where? For example: SQL, data modeling, Python, TypeScript, DSA, system design, infrastructure/CI/CD. Where would you focus? With my background, where would you spend the next 6–12 months? How deep should I go? Are books like Kimball’s Data Warehouse Toolkit or Designing Data-Intensive Applications still worth the time? Or is it better to learn by building with AI and fill gaps as they come up? DSA and LeetCode: worth it for someone on the data engineering or app dev path, for interviews or for real understanding? Or is that time better spent elsewhere? Generalist or specialist? Should I keep spreading across data engineering, app dev, and some data science? Or pick one and go deep, and if so, which has the best long-term odds? I’d especially like to hear from people who’ve hired recently or have long term experience in software. submitted by /u/TheOnlinePolak
Originally posted by u/TheOnlinePolak on r/ClaudeCode