Production Python for Data Engineers Real Interview Prep

Catch AI coding mistakes like a senior engineer, while building one real pipeline - typing, testing, idempotency, CI/CD
What you'll learn

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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

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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

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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

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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

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