Data Engineering
Data pipelines, warehousing, ETL/ELT, streaming, and analytics infrastructure.
Overview
The complete path to building scalable data infrastructure that powers analytics, ML, and business intelligence. This roadmap covers SQL mastery, data modeling, pipeline orchestration with Airflow and dbt, streaming systems with Kafka and Flink, cloud data platforms, and the modern data stack. Each phase is sequenced from foundations through increasingly complex systems, so you build a coherent understanding of how data flows from source to insight.
Data engineering has become one of the fastest-growing and highest-demand specializations in technology. Every company that uses data — which is every company — needs engineers who can build reliable pipelines to collect, transform, store, and serve data at scale. Data engineers are the foundation that data scientists, ML engineers, analysts, and business teams depend on. Without clean, timely, well-modeled data, ML models cannot train, dashboards show stale numbers, and business decisions are made on guesswork. Companies like Airbnb, Netflix, Uber, and Spotify run hundreds of thousands of data pipelines and employ large data engineering teams.
What makes this roadmap practical is its focus on the modern data stack — the set of tools that have become industry standard. You will learn dbt for SQL-based transformations that are version-controlled and tested, Airflow for orchestrating complex pipeline DAGs, Spark for distributed processing of datasets too large for a single machine, Kafka for real-time event streaming, and cloud warehouses like Snowflake and BigQuery that separate storage from compute. The roadmap also covers data quality frameworks like Great Expectations, because in production, catching bad data before it reaches stakeholders is just as important as building the pipeline itself.
By the end of this roadmap, you will be able to design and build batch and streaming data pipelines, model data warehouses using dimensional modeling techniques, orchestrate complex workflows with proper monitoring and alerting, optimize query performance on large datasets, and discuss data architecture decisions confidently in interviews at data-driven companies.
Interview questions
- Data Engineering vs Data Science?
- Which cloud platform should I learn?
- How important is SQL for data engineering?
- What is dbt and why does everyone use it?
- Batch or streaming — which should I learn first?
- What does a data engineering interview look like?
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