Career as a Data Engineer

Data engineers are specialized professionals who design and maintain systems that gather, process and convert raw data into usable information for analysis and decision-making. Their work forms the backbone of modern data analytics, enabling businesses to unlock insights from vast amounts of information for strategic planning and innovation.

Working as a Data Engineer can mean a range of exciting responsibilities. We have collected some of the most common tasks that any Data Engineer can expect in their day-to-day work.

Responsibilities of Data Engineers

  • Proficiency in designing, building, and optimizing robust data pipelines,
  • Experience collaborating with cross-functional teams, including product managers and designers, through the entire software development lifecycle.
  • Supported various business functions such as operations, finance, HR, sales, and procurement and worked within technology groups to drive data processing and analysis projects.
  • Architecting data solutions emphasizing simplicity, reliability, and developer experience.
  • Data Acquisition - develop and use tools to automate the downloading of large datasets.
  • Building essential scraping tools.
  • Data Set Processing - preprocess, clean, and verify the data integrity used for analysis.
  • Ability to read specifications and translate them into test designs and test automation.
  • Conceptualizing and implementing API solutions and maintaining good relationships with key internal stakeholders.
  • Collaboration skills with senior IT leadership teams and the ability to align solutions and services with company strategies.
  • Proficiency in troubleshooting, problem-solving, and understanding the data landscape and integration interfaces.
  • Proficiency in cloud platforms
  • Skills in writing unit and integration tests, working with DevOps on CI/CD and performing code reviews
  • Technical leadership and mentorship capabilities.
  • Continuous learning and willingness to learn domain knowledge.

Tech Stack of Data Engineers

  • What technical skills are required for a successful career as a data engineer? We reviewed hundreds of job descriptions from more than 1000 AI companies and identified the most common technical skills and requirements valuable for data engineers.
  • Proficiency in programming languages, particularly Python, Scala (PySpark preferred), and Java.
  • Extensive experience with big data technologies, notably Scala, Spark, the Spark Engine, Spark Dataset API, and Hadoop.
  • Strong background in machine learning, including experience with frameworks like TensorFlow and PyTorch.
  • Hands-on experience with cloud platforms, especially Azure and AWS.
  • Knowledge of Azure Analytics Services (Azure Databricks, Azure Data Factory, FiveTran, MS Purview) and AWS services.
  • Power BI knowledge
  • Experience with data modeling concepts, best practices, JSON, XML, NoSQL databases (e.g., MongoDB).
  • Experience in CI/CD pipelines, unit testing (JUnit, Mockito, Jenkins), Maven, and Git.
  • Familiarity with workflow management tools like Airflow and Jenkins.
  • Proficiency with version control platforms like GitHub.
  • Experience with containerization, governance, and security for data infrastructure.
  • Experience with streaming transport and stream processing (e.g., Kafka, Pub/Sub) (good to have).


Most Data Engineer profiles require some kind of formal education. Based on our research, most roles have one of the following requirements.

  • Bachelor's degree in Computer Science, Information Systems Management, Data Science, Engineering, Mathematics, or a related field. Alternatively, some employers will accept a general quantitative degree.
  • A Master’s degree in a relevant field is advantageous, with 3+ years of technical experience preferred.
  • Prior experience in a data-heavy backend software engineering role can substitute for university degrees.

Are you ready for a career in data engineering?

Explore the latest Data Engineer Jobs on Getin.AI!