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dimensions:
width: 968
height: 4750
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courses:
- title: 'Complete Course to Master SQL'
description: 'Learn SQL from scratch with this comprehensive course'
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headline: 'Data Engineer Roadmap'
description: 'Learn how to become an Data Engineer with this interactive step by step guide in 2025. We also have resources and short descriptions attached to the roadmap items so you can get everything you want to learn in one place.'
imageUrl: 'https://roadmap.sh/roadmaps/data-engineer.png'
datePublished: '2024-04-02'
dateModified: '2024-04-02'
question:
title: 'What is Data Engineering?'
description: |
Data engineering is the practice of designing, building, and maintaining the infrastructure and systems that enable the collection, storage, processing, and analysis of data at scale. It serves as the foundation that allows data scientists and analysts to work with clean, reliable, and accessible data.
In a nutshell, data engineering involves building data pipelines that extract data from various sources, transform it into usable formats, and load it into data warehouses or data lakes (commonly known as ETL/ELT processes). While data engineers are often confused with data scientists or data analysts, data engineers focus on the infrastructure and architecture that makes data analysis possible.
By using tools like Apache Spark, Airflow, Kafka, and cloud platforms like AWS, GCP, or Azure, data engineers create robust systems that can handle massive volumes of data, ensure data quality, and maintain high performance. They build the highways on which data travels throughout an organization.
In essence, it's all about using engineering principles, programming skills, and distributed systems knowledge to create scalable data architectures that transform raw data into valuable assets for business intelligence and machine learning applications.
## What does a Data Engineer do?
A data engineer designs and builds the systems that collect, store, and process large volumes of data. The role is highly technical and focuses on creating reliable, scalable infrastructure that enables data-driven decision making across the organization.
To be more specific, a data engineer's work revolves around building and maintaining data pipelines that extract data from various sources (APIs, databases, streaming services, files), transform it to meet business requirements, and load it into destination systems like data warehouses, data lakes, or real-time processing platforms. They ensure data quality, implement data governance practices, and optimize performance for large-scale data processing.
With a strong foundation in software engineering and distributed systems, data engineers use programming languages like Python, Scala, or Java, along with big data technologies like Apache Spark, Hadoop, and Kafka. They work with cloud platforms to build scalable architectures and implement DataOps practices for continuous integration and deployment of data pipelines.
By the nature of their work, data engineers collaborate closely with data scientists, analysts, and business stakeholders to understand data requirements and deliver reliable data infrastructure. They implement monitoring, alerting, and data quality checks to ensure the data pipelines are robust, secure, and compliant with regulations like GDPR or CCPA.
## What skills are required for Data Engineering?
Data engineering requires a strong combination of technical skills spanning software engineering, database management, and distributed systems. Data engineers must start by mastering programming languages like Python, SQL, and often Scala or Java for building robust data pipelines and working with big data frameworks.
Database expertise is crucial—you need deep knowledge of both SQL databases (PostgreSQL, MySQL) and NoSQL systems (MongoDB, Cassandra, DynamoDB). Understanding data modeling, normalization, indexing, and query optimization is essential. You should also be proficient in building and optimizing data warehouses using platforms like Snowflake, BigQuery, or Redshift.
Big data technologies form the core of modern data engineering. This includes distributed processing frameworks like Apache Spark and Hadoop, streaming platforms like Apache Kafka and Kinesis, and workflow orchestration tools like Apache Airflow or Prefect. Cloud platform expertise (AWS, GCP, Azure) is increasingly important, including services like S3, EMR, Dataflow, and Azure Data Factory.
Beyond technical skills, data engineers need strong software engineering practices including version control (Git), CI/CD pipelines, containerization (Docker, Kubernetes), and infrastructure as code (Terraform, CloudFormation). Understanding data governance, security best practices, and compliance requirements is also critical for building enterprise-grade data systems.
datePublished: '2025-08-13'
dateModified: '2025-08-13'
seo:
title: 'Data Engineer Roadmap'
description: 'Learn to become an Data Engineer using this roadmap. Community driven, articles, resources, guides, interview questions, quizzes for modern data engineers.'