What does a Data Engineer do?
A Data Engineer is responsible for the design, construction, installation, testing, and maintenance of highly scalable data management systems. They build high-performance algorithms, predictive models, and prototypes that can ingest, distribute, and analyze large amounts of data from varying sources, ensuring it is accessible and usable for data scientists and analysts.
Key duties & responsibilities
Data Engineers in the IT & Development field handle essential tasks and contribute significantly to achieving team and organizational goals. Here are some of their primary responsibilities:
- Develop, construct, test, and maintain architectures, such as databases and large-scale processing systems.
- Employ a variety of languages and tools to marry systems together or try to hunt down opportunities to improve data reliability, efficiency, and quality.
- Use complex data sets to build data pipelines and integrate data from various resources.
- Use data to discover tasks that can be automated.
- Prepare data for predictive and prescriptive modeling.
- Find hidden patterns using data.
- Use data to make business decisions.
- Employ an array of technological tools and techniques to process big data and ensure data privacy and security.
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How to become a Data Engineer
Launching a career as a Data Engineer requires a specific education. We outline the essential qualifications, skills, and steps to enter this field.
Qualification
- Bachelor’s or Master’s degree in Computer Science, Statistics, Informatics, Information Systems, or another quantitative field.
- Experience with big data tools: Hadoop, Spark, Kafka, etc.
- Experience with data pipeline and workflow management tools.
- Experience with object-oriented/object function scripting languages: Python, Java, C++, Scala, etc.
Requirements & skills
- Advanced working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases.
- Experience building and optimizing ‘big data’ data pipelines, architectures, and data sets.
- Strong analytic skills related to working with unstructured datasets.
- Build processes supporting data transformation, data structures, metadata, dependency, and workload management.
- A successful history of manipulating, processing, and extracting value from large disconnected datasets.
- Working knowledge of message queuing, stream processing, and highly scalable ‘big data’ data stores.
- Strong project management and organizational skills.
Data Engineer salary guide
Wondering what Data Engineers earn? Explore salary ranges by experience, and career tips to maximize your earning potential.
The average salary for a Data Engineer in the U.S. is approximately $122,450 per year and can vary from entry-level to senior positions. Data Engineers may receive a wide range of benefits.
Data Engineer salary career steps
- Entry Level0-2 years $91,838
- Mid Level3-5 years $110,205
- Experienced6-10 years $122,450
- Senior11+ years $134,695
- Veteran20+ years $153,063
Based on public data from the U.S. Bureau of Labor Statistics and Payscale. Career-level figures are estimates derived from the national average; actual pay varies by employer, region and experience.
How much can you earn as a Data Engineer?
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How to advance your career
Data Engineers typically begin their careers with a bachelor's degree in computer science, engineering, mathematics, or a related field. Entry-level positions in data engineering may include roles such as Data Analyst, Database Developer, or Software Engineer, where professionals gain foundational experience in data management, database design, and programming.
With experience, Data Engineers can advance to roles such as Data Engineer, Senior Data Engineer, or Data Architect, where they lead data engineering projects, design and optimize data pipelines, and implement scalable data solutions.
Continued education, staying updated on emerging technologies such as big data frameworks, cloud platforms, and machine learning, and building a portfolio of projects are essential for career growth in data engineering.
Typical work environment
Data Engineers work in various industries, including technology, finance, healthcare, e-commerce, and entertainment. They collaborate closely with data scientists, business analysts, software developers, and other stakeholders to design, build, and maintain data infrastructure and systems.
Data Engineers may use programming languages such as Python, SQL, or Java, as well as data processing frameworks such as Apache Hadoop, Apache Spark, or Apache Kafka, to develop and deploy data pipelines, ETL processes, and data integration solutions.
The work environment for Data Engineers is fast-paced and dynamic, with professionals often working on multiple projects simultaneously and adapting to evolving business requirements and technology trends.
While standard business hours are typical, Data Engineers may need to work evenings, weekends, or overtime to meet project deadlines or address urgent issues. Overall, being a Data Engineer offers the opportunity to work with cutting-edge technologies, solve complex data problems, and contribute to the success of data-driven initiatives that drive business growth and innovation.
About this profile
This profile for a Data Engineer is based on public sources such as the BLS and Payscale and was structured with the help of AI. It is intended as a general orientation — duties, requirements and salaries can differ by employer, country and experience.
