Production Python for Data Engineers: Real Interview Prep

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.