Data Engineering Courses
Interview-first courses designed to teach you how data engineering hiring managers actually evaluate candidates. Not just theory — real skills.
SQL for Data Engineering Interviews — From Zero to FAANG
The only SQL course built entirely from real Data Engineering interview rounds at Meta, Amazon, Google, and 10+ top companies. 11 chapters. 85+ exercises. Interview-ready in 15 hours.
- Solve 85+ SQL Problems Under Pressure: Practice in a real interview-style IDE with time constraints, company-tagged problems, and hidden test cases — exactly like the real thing.
- Master Every JOIN Type Interviewers Test: From INNER and LEFT to Self Joins, Exclusion Joins, and Multi-Table Chains — the JOIN module alone covers 8 types with 24 hands-on exercises.
- Crack Window Function Rounds — The #1 Senior DE Filter: ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, NTILE, FIRST_VALUE, Window Framing — 32 exercises covering every pattern Amazon, Meta, and Atlassian throw at candidates.
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Python for Data Engineering Interviews — The Complete Fundamentals
Master the Python fundamentals that Data Engineering interviewers actually test. 14 chapters, 70+ hands-on coding exercises, and real interview problems — from variables to OOP, regex to JSON parsing.
- Write Interview-Ready Python Code Under Pressure — Practice 70+ problems in a real IDE with test cases, just like the actual technical screen.
- Master Python Data Structures DEs Use Daily — Lists, tuples, sets, and dictionaries with real-world data pipeline scenarios.
- Manipulate Strings, Dates & Regex Like a Pro — The string parsing and pattern matching skills interviewers test in every Python round.
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Apache Spark Internals for Data Engineering Interviews
Master the Spark internals that interviewers ask about — runtime architecture, execution model, memory management, and 10+ performance tuning techniques. The theory course every Sr. DE candidate needs.
- Explain Spark's Runtime Architecture End-to-End — the whiteboard question in every Spark interview
- Break Down Jobs, Stages, Tasks & Shuffles on Sight — the most tested concept
- Diagnose Memory Issues Like a Production Engineer — OOM debugging at 2am
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ETL System Design for Data Engineering Interviews
Learn how to design scalable ETL systems from scratch — batch pipelines, streaming architectures, and the exact system design frameworks top companies test in Data Engineering interviews.
- Design Batch & Streaming Architectures from Requirements — Given a business problem, decide between batch, streaming, or lambda architecture and justify your choice — the first question in every system design round.
- Choose the Right Tool for Every Layer of the Pipeline — Spark vs Flink, Kafka vs Kinesis, Airflow vs Step Functions, S3 vs Redshift — the technology selection questions interviewers use to test depth.
- Architect End-to-End ETL Pipelines at Scale — Design multi-stage pipelines with ingestion, transformation, storage, and serving layers — the full architecture diagram interviewers expect.
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Data Modeling for Data Engineering Interviews
Skip the junk, and learn the essential Data Modeling foundations like OLAP, OLTP, Star vs Snowflake Schema, SCD's, Normal Forms, CDC and so on.
- Data Modeling
PySpark Fundamentals
Learn the foundations of PySpark, the pythonic way of writing Apache Spark code. A key skill required by many Data Engineering roles.
- Pyspark
Product Sense and Metrics for Data Engineering Interviews
One stop shop for all Product Sense essentials for Data Engineering Interviews. We will cover all foundations right from definitions of a Metric, KPI etc. to frameworks like HEART, AAARP and our very own AATHERRR framework.
- Product Sense
Behavior Interview Prep for Data Engineering Interviews
Master STAR interviewing format, How to answer Behavior Interview questions effectively, How to present your impact and achievements and, learn essential communication patterns for Interview Success.
- Behavior Interview
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