Data Engineering Interview Prep: SQL, Spark & System Design

Descripción

You have two or three years of data engineering under your belt, you can build pipelines that run — but the interview still feels like a different game. The recruiter sends a SQL screen, then a Spark performance grilling, then a system-design whiteboard, and you're never quite sure what they're actually grading. This course closes that gap and gets you to the offer. You'll walk into any data engineering interview ready for every round. We decode each topic as what the interviewer is really testing — not just the right answer, but the traps that sink candidates and the exact difference between a junior response and a senior one. That signal is the wedge that gets you leveled up and paid more. What you'll work through: • SQL, four rounds deep — joins and the classic trap questions, window functions (the #1 filter), advanced patterns like gaps-and-islands and top-N-per-group, and the "why is this slow?" optimization round. • Data modeling and warehousing — dimensional design at the whiteboard, slowly changing dimensions, and defending the grain you chose. • Python and PySpark — idiomatic data wrangling, the distributed mental model, and the Spark internals round: shuffles, skew, AQE, and broadcast joins, explained like someone who has actually debugged them. • Pipelines, streaming and storage — ETL/ELT that survives production, the Kafka conversation, and Parquet/Delta/Iceberg file-format questions. • Distributed systems, data quality and cloud platforms — the fundamentals DEs get quizzed on, testing and reliability, and production-grade Snowflake and Databricks answers. • System design — a repeatable framework, then real prompts solved end to end. • The rounds nobody preps for — the coding/DSA round DEs still face, take-homes and live SQL tests, and the behavioral + project deep-dive with STAR stories that demonstrate ownership. It ends with a full mock-interview gauntlet capstone, so you rehearse the entire loop before it counts. Twenty-two modules, seventy-five focused lessons — built for the early-to-mid-career data engineer who's done waiting to get picked. Enroll, do the reps, and land the offer.

Lo que aprenderás

• Answer any SQL screen — window functions, gaps-and-islands, and top-N-per-group — cold and under pressure.
• Explain Spark internals (shuffles, skew, AQE, broadcast joins) like someone who has actually debugged them in production.
• Design a data warehouse or streaming pipeline on a whiteboard using a repeatable, reusable framework.
• Model dimensional schemas and slowly changing dimensions, and defend the grain you chose.
• Diagnose "why is this query slow?" and walk an interviewer through the optimization, step by step.
• Answer Snowflake and Databricks platform questions with production-grade detail.
• Handle take-homes, live SQL tests, and the coding/DSA round without freezing.
• Land the behavioral and project deep-dive with STAR stories that signal senior-level ownership.

Requisitos

• Roughly 1–3 years of hands-on data engineering or analytics-engineering experience (you've built or maintained real pipelines).
• Working SQL — you can write joins and aggregations; we take you from there to interview-grade.
• Basic Python familiarity; prior Spark or PySpark exposure helps but isn't required.
• A free Snowflake and/or Databricks trial account if you want to run the platform examples (optional).