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Roles directory / Data Engineering

Data Engineer


Data engineers are responsible for designing, building, and maintaining data pipelines and warehouses that enable efficient and secure storage and retrieval of large data sets. They use their knowledge of database technologies, data modeling, and data integration to ensure optimal data processing and accessibility.

Also known as:
Software Data Engineer


Typical years of experience


Data Engineer

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What is data engineering?

Companies of every size and industry use data to make business decisions. To do so, they employ data scientists and data analysts to process and analyze raw data to gain actionable insights. But before these analytical professionals can work with this data, someone needs to capture it.

Data engineering is the process of building systems that collect, manage, and convert raw data into usable information. The concept of data engineering is fairly new, with roles in this field becoming more widespread around 2011. However, as a discipline, data engineering evolved from the more established field of information engineering, which dates back to 1976.

What does a data engineer do?

Data engineers are professionals responsible for designing, developing, and managing the data architecture, infrastructure, and tools necessary for collecting, storing, processing, and analyzing large volumes of data. Their role is crucial in enabling organizations to make informed decisions based on data-driven insights.

On a more technical level, the responsibilities of data engineers include:

Data architecture design

  • Developing the overall architecture of the data systems.
  • Creating data models, schemas, and structures to organize and store data efficiently.

Data pipeline development

  • Building and maintaining data pipelines for the extraction, transformation, and loading (ETL) of data from various sources to the data warehouse.
  • Ensuring the smooth flow of data between systems and components.

Data integration

  • Integrating data from different sources and formats to provide a unified view.
  • Collaborating with data scientists, analysts, and other stakeholders to understand data requirements.

Data storage management

  • Selecting appropriate storage solutions based on data requirements (like relational databases, NoSQL databases, or data lakes).
  • Optimizing data storage for performance, scalability, and cost-effectiveness.

Data quality and governance

  • Implementing data quality checks and ensuring the integrity and accuracy of the data.
  • Establishing and enforcing data governance policies and best practices.


  • Implementing security measures to protect sensitive data.
  • Ensuring compliance with data privacy regulations and industry standards.

What kinds of companies hire data engineers?

As organizations increasingly recognize the importance of data, data engineers have become in demand across a wide range of industries. Companies of every size, from startups to large enterprises, hire data engineers to build and maintain their data infrastructure. 

To name a few, these industries and applications include:

  • Technology Companies: Companies in the technology sector, including software development firms, hire data engineers to manage and analyze the vast amounts of data generated by their products and services.
  • Financial Services: Banks, insurance companies, and other financial institutions employ data engineers to handle and analyze financial data, detect fraud, and ensure compliance with regulations.
  • Healthcare: Healthcare organizations use data engineers to manage and analyze patient records, medical data, and other healthcare data.
  • Retail and E-commerce: Retailers and e-commerce companies hire data engineers to analyze customer behavior, manage inventory, and optimize supply chain operations.
  • Manufacturing and Logistics: Companies in manufacturing and logistics leverage data engineers to optimize production processes, manage supply chain data, and enhance overall operational efficiency.
  • Telecommunications: Telecom companies use data engineers to manage and analyze data related to network performance, customer usage patterns, and infrastructure optimization.
  • Entertainment and Media: Entertainment and media companies employ data engineers to manage and analyze user engagement data, content consumption patterns, and advertising effectiveness.

Data engineer skills & qualifications

Data engineers require a combination of technical and soft skills to effectively design, build, and maintain data infrastructure. Here is a list of skills and qualifications commonly sought after for data engineering roles:

Technical skills

  • Programming Languages: Proficiency in languages commonly used in data engineering, such as Python, Java, or Scala.
  • SQL (Structured Query Language): Proficient in writing complex SQL queries for data analysis and manipulation.
  • Database Management: Experience with both relational databases (MySQL, PostgreSQL) and NoSQL databases (MongoDB, Cassandra).
  • Big Data Technologies: Familiarity with big data processing frameworks, such as Apache Hadoop and Apache Spark.
  • Data Modeling: Ability to design and implement data models, schemas, and structures for efficient storage and retrieval.
  • ETL (Extract, Transform, Load): Expertise in building and maintaining ETL pipelines to move and transform data between systems.
  • Data Warehousing: Knowledge of data warehouse solutions, such as Amazon Redshift, Google BigQuery, or Snowflake.
  • Data Integration: Experience integrating data from various sources to provide a unified view.
  • Data Quality Management: Skills in implementing data quality checks and ensuring data accuracy and consistency.
  • Cloud Platforms: Proficiency in cloud platforms like AWS, Azure, or Google Cloud Platform for building scalable and reliable data solutions.
  • Version Control: Familiarity with version control systems like Git for managing code and configurations.
  • Scripting: Strong scripting skills for automation tasks and data manipulation (Bash).
  • Containerization: Knowledge of containerization technologies like Docker for packaging and deploying applications.
  • Workflow Orchestration: Experience with workflow orchestration tools such as Apache Airflow or Luigi.

Soft skills

In addition to technical skills, data engineers benefit from a variety of soft skills that contribute to their effectiveness in the workplace. Key soft skills include:

Experience & education

Data engineering roles typically require a combination of education and practical experience. The specific requirements can vary based on the employer, the complexity of the projects, and industry expectations. Here are some general guidelines for experience and education for data engineers:


  • Bachelor’s Degree: Most data engineers have a bachelor’s degree in a relevant field such as Computer Science, Information Technology, or a related discipline. About 89% of data engineers in the U.S., for example, have a bachelor’s, master’s, or doctorate degree.
  • Bootcamps and Coding Schools: Coding bootcamps and short-term coding schools offer intensive, focused training in data engineering. These programs are often an alternative for individuals looking to enter the field quickly, and they cover practical skills required for data engineering.
  • Online Courses and Certifications: Online platforms like Coursera, Udacity, and others offer courses and certifications in data engineering. These can be valuable for individuals seeking flexible, self-paced learning options.


  • On-the-job experience: Internships and full-time roles provide hands-on experience and exposure to real-world projects. This practical experience is highly valuable in developing skills and understanding industry best practices.
  • Personal Projects: Building personal projects or contributing to open-source projects can demonstrate initiative and a genuine passion for data engineering. This can be particularly beneficial for self-taught engineers or those with non-traditional educational backgrounds.
  • Continuous Learning: The field of data engineering is constantly evolving. Continuous learning and keeping up with trends is essential for staying relevant and competitive.