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Build the High-Performance Pipelines that Power Enterprise Analytics and AI Models.
AI and Machine Learning models are only as good as the data feeding them. Data Engineering is one of the highest-paying and most critically undersupplied specializations in the technology sector.
This 90-day bootcamp focuses on the Modern Data Stack (MDS). You will master dimensional data modeling (Kimball methodology), cloud data warehousing with Snowflake and BigQuery, production transformation workflows with dbt (data build tool), workflow orchestration with Apache Airflow, and automated data testing.
You will complete the program by engineering an end-to-end, event-driven ELT pipeline that extracts streaming data, transforms it modularly with dbt, and schedules it automatically with Airflow.
Design scalable dimensional models with Fact and Dimension tables and Kimball methodology
Architect cloud data warehouses using Snowflake and BigQuery with clustering and partition optimization
Build modular, version-controlled SQL data transformation pipelines with dbt (models, tests, docs)
Orchestrate complex Directed Acyclic Graphs (DAGs) using Apache Airflow with retries and alerts
Implement automated data quality checks to prevent pipeline failures and schema drift
Our industry-aligned syllabus is partitioned into 4 high-impact milestones, taking you systematically from core concepts to enterprise deployment.
Master dimensional architecture, Kimball methodology, and advanced analytical SQL constructs.
You will build, test, and deploy production-grade software applications to create a verifiable GitHub portfolio that proves your capabilities to hiring managers.
Production cloud data warehouse pipeline with Snowflake, dbt transformations & Apache Airflow.
A production-grade ELT data engineering pipeline that ingests continuous streaming e-commerce events into cloud object storage, loads them into Snowflake via staging stages, transforms raw logs into Kimball dimensional marts using dbt with automated data quality assertions, and orchestrates the entire workflow on an Apache Airflow cluster with automated Slack incident notifications on task failure.
Demonstrates production competency in the Modern Data Stack, data warehouse modeling, testing, and enterprise pipeline orchestration.
Everything you need to know about the Modern Data Engineering & Cloud Data Warehousing program, mentorship, and credentials.
Data Engineers build the plumbing, infrastructure, and pipelines that extract, clean, and organize data at scale. Data Scientists use the clean data provided by Data Engineers to build statistical models and predictions.