Data Engineer Job Descriptions, Average Salary, Interview Questions

What Does a Data Engineer Do?

Data engineers design, build, and maintain the infrastructure that stores, processes, and analyzes large amounts of data. Data engineers are responsible for the overall architectures for data pipelines that enable organizations to make data-driven decisions by collecting and utilizing data. 

As part of their role as data engineers, professionals will work closely with data scientists, business analysts, and other stakeholders to understand their data needs and design systems that meet those needs. In addition, they ensure that data is easily accessible and usable by other organization members and that privacy, security, and governance standards are met.

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National Average Salary

Data engineer salaries vary by experience, industry, organization size, and geography. Click below to explore salaries by local market.

The average national salary for a Data Engineer is:

$113,965

Data Engineer Job Descriptions

When it comes to recruiting a data engineer, having the right job description can make a big difference. Here are some real world job descriptions you can use as templates for your next opening.

Candidate Certifications to Look For

  • IBM Data Engineering Professional Certificate. The certificate is for entry-level candidates looking to stand out from their peers and develop job-ready data engineering skills. The self-paced online courses give candidates the essential skills they need to work with a variety of tools and databases to design, deploy, and manage structured and unstructured data. The course uses Python programming language and Linux/UNIX shell scripts where they’ll extract, transform and load (ETL) data. Candidates will gain a working knowledge of relational databases (RDBMS) and query data using SQL statements, among other things. With numerous labs & projects, they’ll get hands-on experience utilizing the concepts and skills they learn. There are no eligibility requirements for this credential. 
  • Cloudera Certified Data Engineer (CCP). If candidates are experienced open-source developers, earning the Cloudera Certified Data Engineer credential will demonstrate their ability to perform the core competencies required to absorb, transform, store, and analyze data in Cloudera’s CDH environment. Candidates interested in the CCP Data Engineer credential should have in-depth experience developing data engineering solutions. The program includes transferring data, storing data, data analysis, and workflow.
  • Google Cloud Certified Professional Data Engineer. The Google Cloud Certified Professional Data engineer credential ensures that candidates can design, build, secure, and monitor data processing systems, emphasizing compliance, scalability, efficiency, reliability, and portability. The exam assesses their skills in designing data processing systems, using machine learning models, ensuring solution quality, and using data processing systems. There are no prerequisites or requirements for this credential, however, it is recommended that candidates have 3+ years of industry experience, including 1+ years designing and managing solutions using Google Cloud.

Sample Interview Questions

  • Which ETL Tools are you familiar with? 
  • What skills are important for a data engineer?
  • What data engineering platforms and software are you familiar with?
  • Which computer languages do you have experience using?
  • How do you create reliable data pipelines?
  • What is the difference between structured and unstructured data?
  • How would you deploy a big data solution?
  • Have you engineered a distributed system? How did you engineer it?
  • Have you used data modeling?
  • Which frameworks and applications are essential for a data engineer?
  • Are you more database or pipeline-centric?
  • How would you validate a data migration from one database to another?
  • What are the pros and cons of cloud computing?
  • How would you prepare to develop a new product?
  • Which Python libraries would you use for efficient data processing?
  • How would you deal with duplicate data points in an SQL query?
  • How would you plan to add more capacity to the data processing architecture to accommodate an expected increase in data volume?
  • What is the difference between relational vs. non-relational databases?
  • Can you explain the components of a Hadoop application?

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