Data Engineer

A 50-hour curriculum for data engineers ready to operate at senior+ levels at Snowflake-, Databricks-, and BigQuery-shop scale. You will build production-grade ELT pipelines with dbt-core 1.8+, design lakehouse architectures on Iceberg 1.4+ / Delta Lake 3.x, run sub-second streaming with Kafka 3.7+ and Flink 1.19, and operationalize data quality with Great Expectations, Soda, and data contracts. Outcome: you can rebuild a 100 TB warehouse from scratch, justify every tool choice with cost numbers, and pass senior data engineering loops at Stripe, Airbnb, Lyft, Shopify, and Netflix.

23 modules across 5 stages50 hours of content

Course curriculum

Stage 1

Foundations

1
SQL Mastery
180 min
2
Data Modeling Fundamentals
180 min
3
Python for Data Engineering
180 min

Stage 2

Pipeline Engineering

1
ETL/ELT Pipeline Patterns
180 min
2
Workflow Orchestration with Airflow
180 min
3
Data Transformations with dbt
180 min
4
Data Quality & Testing
120 min

Stage 3

Warehousing & Data Lakes

1
Dimensional Modeling & Star Schemas
180 min
2
Cloud Data Warehouses
180 min
3
Data Lake Architecture
180 min
4
Data Governance & Cataloging
120 min
5
Lakehouse Architecture (Iceberg, Delta, Hudi)
180 min
6
Real-Time Analytics & Streaming SQL
180 min

Stage 4

Distributed Processing & Streaming

1
Apache Spark Deep Dive
240 min
2
Apache Kafka & Event Streaming
180 min
3
Streaming vs Batch Processing
180 min
4
Data Contracts & Schema Evolution
120 min
5
Interview Preparation for Data Engineers
360 min

Stage 5

Expert Specialization

1
CI/CD for Data Pipelines
120 min
2
Pipeline Monitoring & Observability
120 min
3
Data Mesh & Decentralized Architecture
120 min
4
System Design & Technical Leadership
180 min
5
Data Platform Cost Optimization
120 min