Data Engineer
Builds and maintains the data pipelines, infrastructure, and systems that collect, store, transform, and deliver the data that powers AI and analytics.
Overview
A data engineer designs and builds the plumbing of the modern data organization, creating pipelines that ingest data from diverse sources, transform it into reliable and usable formats, and deliver it to the right destinations for analysis, reporting, and machine learning. They think deeply about reliability, scalability, and data quality, and they work closely with data scientists, analysts, and ML engineers to ensure the data those teams depend on is timely and trustworthy. As AI adoption accelerates, data engineering has become one of the most in-demand technical disciplines in the industry.
Beginner roadmap
Phase 1: SQL and Data FundamentalsWeeks 1-6
Master SQL beyond basic queries to include window functions, CTEs, and query optimization, and learn data modeling concepts including normalization and dimensional modeling.
Phase 2: Pipeline DevelopmentWeeks 7-14
Build batch pipelines with orchestration tools, practice ETL and ELT patterns, and learn to handle common data quality issues like duplicates, nulls, and schema drift.
Phase 3: Cloud and ScaleWeeks 15-20
Work with cloud data warehouse and lake services, learn distributed processing for large-scale data, and understand how to design for cost efficiency and performance.
Phase 4: Streaming and Production QualityWeeks 21-26
Build a streaming pipeline, add data quality checks and monitoring, and document a complete data platform project for your portfolio.
Portfolio ideas
- An end-to-end pipeline that ingests data from a public API, transforms it, and loads it into an analytical store.
- A data modeling project that designs a dimensional schema for a realistic business scenario.
- A data quality framework with automated checks, alerting, and documentation of the decisions made.
- A streaming pipeline that processes real-time events and makes them available for downstream use.
- A cost and performance analysis of a data workload, with documented optimizations and their measured impact.
Salary & sources
Salary ranges vary widely by region, seniority, industry, and company. Check current data on reputable salary aggregators (placeholder - verify before publishing).
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Frequently asked questions
Data engineers build and maintain the infrastructure and pipelines that make data available, clean, and reliable. Data scientists use that data to build models and extract insights. The two roles depend heavily on each other, and in smaller organizations one person may do both.
SQL is foundational. Data engineers write complex queries, design schemas, optimize query performance, and build transformations in SQL every day. It is arguably the single most important language for the role.
Understanding managed data warehouse and data lake services on at least one major cloud provider is a good starting point, along with object storage, managed streaming services, and basic compute. Most modern data stacks are cloud-native.
AI and ML systems are only as good as the data that feeds them. Data engineers build the pipelines that deliver training data, feature stores that serve models at inference time, and the infrastructure that tracks data quality and lineage.
Related career guides
Machine Learning Engineer
Builds, trains, and deploys machine learning models and the data pipelines and infrastructure that keep them running reliably in production.
Data Scientist
Analyzes data to uncover insights, builds predictive models, and communicates findings that help organizations make better decisions.
AI Automation Specialist
Designs and builds automated workflows that combine AI with existing tools to save time, reduce errors, and streamline operations.
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