• views: 103
• date: 23 August 2026
• posted by: voska89

Catch AI coding mistakes like a senior engineer, while building one real pipeline — typing, testing, idempotency, CI/CD

What you'll learn

⚡ Structure and package a real Python project the way production teams do, with a proper CLI entry point, not a loose script
⚡ Use strict typing and Pydantic to catch bad data at the door, before it causes a confusing bug downstream
⚡ Build resilient error handling with classified exceptions, backoff-and-jitter retries, and a dead-letter path for bad records
⚡ Make a data pipeline provably idempotent, so running it twice — on purpose or by accident — never duplicates or corrupts data
⚡ Write unit, integration, and property-based tests that actually cover failure paths, not just the happy path
⚡ Add structured logging with a run ID so you can diagnose a failure without rereading code or rerunning the pipeline
⚡ Work with an AI coding assistant using a disciplined closed loop, and catch real, documented mistakes AI tools commonly make
⚡ Practice real senior-level interview questions every module: recall, judgment, live debugging, and AI-code review
⚡ Handle concurrency safely with bounded, profiled async code — fast without overwhelming the systems you depend on
⚡ Manage configuration and secrets the way real teams do: typed, validated, environment-aware, with zero hardcoded values
⚡ Set up a CI pipeline that actually gates what it claims to — failing loudly on lint, type, and test violations, not silently skipping them
⚡ Finish with one complete, working capstone project you can walk an interviewer through — not nine disconnected exercises

Requirements

❗ Comfortable writing and reading Python — functions, classes, and basic error handling
❗ Basic command-line comfort — you don't need to be an expert, everything is walked through step by step
❗ Basic familiarity with git (clone, commit, push) — also covered from scratch during setup if you're new to it
❗ A laptop that can run Docker — Windows, Mac, or Linux all work, and setup instructions assume you're starting from zero
❗ No prior production engineering experience needed — closing that exact gap is the entire point of this course

Description

Most Python courses teach you syntax. This one teaches you how to build the kind of code a senior data engineer is actually expected to ship — code that survives failure, gets reviewed like a real pull request, and holds up under interview-level scrutiny.
If you already know Python but have never built a production data pipeline — one that has to run unattended, recover from failure, and be trusted by a team — this course closes that exact gap.
In this course, you will
✨ Structure and package a real Python project the way production teams do — not a script, an installable tool
✨ Use strict typing and Pydantic to catch bad data before it becomes a confusing bug three functions downstream
✨ Build resilient error handling — retries with backoff and jitter, and a dead-letter path for records that can't be saved
✨ Make a pipeline provably idempotent, so running it twice never corrupts your data
✨ Write tests that actually test failure paths, not just the happy path
✨ Add structured logging you can actually debug from, without rerunning anything
✨ Handle concurrency safely, with bounded, profiled async code
✨ Manage configuration and secrets the way real teams do — typed, validated, never hardcoded
✨ Set up a CI pipeline that actually gates what it claims to gate
One real project, built module by module
You won't jump between disconnected exercises. From day one, you're assigned one capstone project — a pipeline that pulls records from a deliberately unreliable, rate-limited, paginated mock API and lands them safely in a Postgres warehouse. Every module adds one real capability to this same project. By the end, it's a complete system you can walk an interviewer through, not a folder of unrelated homework.
Learn to work with AI without losing your judgment
Every module includes a hands-on round with a real AI coding assistant — but always after you've built the concept by hand first. Your job in each round is to catch a real, documented mistake AI assistants commonly make on that exact topic. By the end, you'll have a personal log of real mistakes you caught and fixed — direct proof, in an interview, that you can work with AI without switching off your own engineering judgment.
Real interview practice, every module
Each module ends with a four-part interview drill: recall, judgment (run as a live back-and-forth, the way real interviews actually go), debugging unfamiliar broken code, and reviewing AI-generated mistakes. The course ends with a full six-round interview simulation.
This course is for you if
✨ You know Python fundamentals but haven't built production systems
✨ You're preparing for mid-to-senior data engineering interviews
✨ You want to close the gap between "code that works" and "code a team can trust"
Prerequisites
Comfortable Python fundamentals, basic command-line and git familiarity, and a laptop that can run Docker. No prior production experience required — that's what this course teaches.

Who this course is for

⭐ Data engineers who know Python but haven't built production systems yet, and want to close that gap deliberately
⭐ Engineers preparing for mid-to-senior data engineering interviews who want real, practiced answers — not memorized theory
⭐ Anyone who's felt the gap between code that works and code a team can trust, and wants a structured way to close it
⭐ Engineers who want real, hands-on practice working with AI coding assistants without losing their own judgment
⭐ Not a good fit if you're brand new to Python itself — this course assumes you can already write and read it comfortably

Homepage

https://www.udemy.com/course/production-python-for-data-engineers

   
   
   

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