Data engineering

17 staged plans for the data engineering interview

Each one is a sequence, argued step by step: what to read, what to build, and why that order. Every step is a chapter or a reviewed exercise that already ships.

Build Your Foundation

8 stages · ~19hData Engineering Roadmap for Beginners: Zero to Data EngineerYou can design, build, schedule and explain a data pipeline end to end.
4 weeks · ~9hYour First 30 Days as a Data EngineerYou can query an unfamiliar schema, put it on a schedule, and model it.
6 moves · ~16hData Analyst to Data Engineer: How to Make the MoveYou can model it, load it incrementally, schedule it, and own it downstream.

Interviewing

6 weeks · ~23hData Engineer Interview Prep for 2–4 Years of ExperienceYou can pass the SQL, Python, modeling, design, Spark and behavioral rounds at mid-level.
8 weeks · ~33hSenior Data Engineer Interview Prep for 5–8 Years of ExperienceYou lead a design, defend one model, and tell ownership stories with real stakes.
7 weeks · ~24hStaff Data Engineer Interview Prep for 8+ Years of ExperienceYou show org-scope impact: designs other teams depend on, and stories with numbers.
4 parts · ~8hHow to Prepare for the SQL Interview RoundYou can take a vague prompt to a defended answer and explain the cost.
4 parts · ~9hHow to Prepare for the Python Data Engineering RoundYou can transform a real dataset, defend the edge cases, and be reviewed.
4 parts · ~9hHow to Prepare for the Data Pipeline Design RoundYou can design a pipeline and defend the reruns, the late data and the cost.
4 parts · ~10hHow to Prepare for the Data Modelling RoundYou can design a model from a vague prompt and defend every choice in it.
4 parts · ~8hHow to Prepare for the Apache Spark Interview RoundYou can narrate any Spark job from its plan, and fix it with evidence.
4 signals · ~10hSenior Data Engineer Interview PreparationYou argue cost, failure and change without being asked to.
3 weeks · ~10hData Engineer Interview Prep in 3 WeeksYou can pass a SQL screen, survive the Spark round, and defend a design out loud.

Hands on Concepts

4 decisions · ~8hHow to Design a Data Warehouse, End to EndYou own a model that loads nightly and that other teams trust.
4 shifts · ~8hReal-Time and Streaming Data EngineeringYou can run a stream in production and explain what it guarantees.
4 phases · ~9hOwning the Data Engineering On-Call PagerYou can take the pager and give the numbers back without a second incident.
4 levers · ~9hSpark Performance Tuning: Make Slow Spark Jobs FastYou can make a slow Spark job cheaper and prove the answer did not change.