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Data Engineering

Pipelines, warehouses, and data infrastructure

Everything you need to get hired as a data engineer. Design and build data pipelines, streaming systems, and analytics infrastructure. Spark, Airflow, dbt, NoSQL, cloud warehouses, and data quality at scale.

~10 phases · ~49 lessons · ~49 labs · 5 projects

No prerequisites — this is a starting track.

Outcomes you will have by the end

• 5 GitHub repos with pipeline code — Batch, NoSQL, streaming, warehouse, and infrastructure projects with documentation and tests.
• 1 production-like data pipeline — End-to-end pipeline deployed on cloud infrastructure with monitoring and alerting.
• Verified certificate of completion — Tied to your completion record. Lists the specific skills, phases, and projects you completed.
• Defense Practice Report — Readiness score across SQL, NoSQL, pipelines, warehouses, streaming, orchestration, and cloud.
• Interview-ready career package — Resume, LinkedIn, data system design prep, and take-home assignment strategies.
• Hands-on labs, not passive videos — Every lesson has a quiz, every phase has a lab, and every project ships with real data workflows.

What you will be able to do

Spark · Airflow · dbt · Snowflake · Kafka · NoSQL

Every phase, every lesson, every project

• Data Engineering Foundations (5 lessons) — free — Python for data, SQL deep dive, data modeling, Git, cloud basics, data lifecycle
• Batch Pipelines (5 lessons) — free — ETL design, Spark, Pandas, data validation, scheduling, idempotency
• Data Warehousing (5 lessons) — Dimensional modeling, Snowflake/BigQuery, dbt, optimization, cost management
• NoSQL & Polyglot Persistence (4 lessons) — CAP theorem & consistency tradeoffs, document stores (MongoDB), key-value & wide-column (DynamoDB, Cassandra), graph databases (Neo4j), choosing the right store per workload
• Streaming Data (5 lessons) — Kafka, stream processing, event-driven architecture, exactly-once semantics
• Orchestration (5 lessons) — Airflow, DAG design, observability, retries, backfills, testing pipelines
• Data Quality & Governance (5 lessons) — Data contracts, lineage, observability, privacy, compliance, great expectations
• Streaming & Real-Time Analytics (5 lessons) — Real-time dashboards, stream joins, windowing, materialized views
• Cloud & Production (5 lessons) — Docker, Kubernetes basics, CI/CD for data, Terraform, cloud cost optimization
• Portfolio & Career (5 lessons) — Data engineering portfolio, resume, system design, take-home prep, interviews

The technologies you will use

Spark · Airflow · dbt · Snowflake · Kafka · PostgreSQL · MongoDB · Docker · AWS · Terraform · Python

Roles this course prepares you for

• Data Engineer ($120k–$175k) — Build and maintain data pipelines, warehouses, and infrastructure that power analytics and ML.
• Analytics Engineer ($110k–$160k) — Transform raw data into clean, tested, documented models that the business can trust.
• Data Platform Engineer ($130k–$190k) — Build the infrastructure other data engineers use — orchestration, observability, governance, and cost management.

What data engineering actually is

Data engineering is the discipline of building the infrastructure that moves, transforms, and stores data at scale. You design pipelines that ingest data from dozens of sources, transform it reliably, and deliver it to analysts, scientists, and products. It is not "writing SQL queries" — it is the engineering layer that makes data trustworthy, available, and useful.

What you do every day

You build batch and streaming pipelines with Spark and Airflow, model data with dbt, monitor pipeline health, and respond to data quality incidents. You design schemas, optimize query performance, and ensure that downstream teams can trust the data they consume.

Why companies hire for this

Every company runs on data. Most cannot trust their own pipelines. The gap is in scalable pipeline design, data quality engineering, and cloud infrastructure. Companies need engineers who can build data systems that do not break at 3am.

What this course is not

It is not a "learn SQL" course. You will not spend weeks on SELECT statements. You will build real pipelines with Spark and Airflow, model data with dbt, stream with Kafka, and deploy to cloud warehouses — and you will prove it with a portfolio of working data systems.

Common questions

When does the Data Engineering course launch?

Targeting late 2026. Pro subscribers get access at launch at no additional cost.

What background do I need?

Comfortable with SQL and basic Python. We cover data pipelines, warehouses, and streaming from first principles.

How is this different from a data science course?

This is data engineering, not data science. You build the pipelines, warehouses, and infrastructure that data scientists and analysts use. Focus is on reliability, scale, and data quality — not ML models.

Do I need a computer science degree?

No. The curriculum covers everything from SQL fundamentals to streaming architecture. You need motivation and consistency, not a CS degree.

Will I learn enough to pass technical interviews?

Yes. Each phase includes interview prep, take-home practice, and data system design scenarios. The final phase is dedicated to portfolio polish and interview readiness.

What if I already know SQL or Python?

You can skip ahead. Phases are self-contained, and the first two are free. Start where you need the most practice.

Key terms in this course

Streaming · Orchestration

Start the Data Engineering course

Create a free account — the opening phases of 24 of 30 courses are free, no credit card. Or see Pro pricing.

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