Jobsity’s Role Spotlight Series: Data Engineers

Written by Donna Kmetz
4 Minutes read
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Welcome to Jobsity’s Role Spotlight Series, where we take you through the ins and outs of the most in-demand tech professionals. 

This week, we’re covering data engineers. What does their job entail? How does someone become a data engineer? What qualities should hiring managers look for in their candidates for the role? And how does data engineering contribute to your industry?

Let’s find out. 

What Is a Data Engineer?

Data engineers are responsible for designing, building, and maintaining everything to do with data infrastructure in an organization. This includes data processing, storage, and retrieval. They work closely with data scientists, analysts, and other teams to understand the company’s data requirements. The goal is to design and maintain systems that can handle large volumes of data reliably, efficiently, and safely.

Data Engineers use various programming languages, database management systems, and data processing frameworks. As data engineering is such an all-encompassing field, data engineers often specialize. Common specialties include developing data pipelines, designing and optimizing databases and data warehouses, and supporting data integration and transformation. 

Data Engineers usually hold a bachelor’s degree in computer science, IT, or a related field. After they’ve finished their bachelor studies, many continue to advanced degrees. This helps them decide how they’d like to specialize and gain the principles and knowledge to do so. 

However, as always, it’s not just about the degrees. Many data engineers learn on the job, gaining relevant skills and experience through certifications, online courses, and company mentorship programs.

What Tools and Platforms do Data Engineers Use?

When applying for an open position data engineers should be familiar with a wide range of tools and platforms, depending on your organization’s needs. 

Data processing frameworks like Apache Hadoop, Apache Spark, and Apache Flink allow for parallel processing of large datasets. Database technologies like MySQL, PostgreSQL, MongoDB, and Cassandra are the foundation for data storage and retrieval

Data warehousing solutions like Amazon Redshift, Google BigQuery, and Snowflake support the consolidation and analysis of vast datasets. ETL tools like Apache NiFi, Talend, and Informatica streamline data integration

Streaming platforms like Apache Kafka, Amazon Kinesis, and Apache Pulsar are necessary for real-time data processing. Version control tools like Git and SVN ensure code integrity, and cloud services like AWS, Azure, and GCP offer scalable infrastructure for data operations

Keep in mind that your company won’t need to use all of these technologies to be successful. A hiring manager should always defer to the relevant departments to decide job description parameters.

Which Industries Need Data Engineers?

Data engineers work across industries. The world runs on data, and while the role itself is demanding, it’s also in high demand:

  • Healthcare: Data Engineers integrate patient data for better care coordination, develop predictive models for disease identification, optimize operational processes, and support medical research with data analysis.

  • Energy: Data engineers perfect energy distribution with smart grid data analysis. They also predict equipment failures to support proper maintenance, integrate renewable energy sources, and assist with energy market analysis.

  • Manufacturing: Data Engineers monitor product quality, support supply chain operations, identify process inefficiencies, and design predictive maintenance techniques.

  • Food: Data engineers design systems for food traceability and safety. They also refine product quality assessment, forecast demand for efficient production, and analyze environmental data for sustainable practices.

  • Finance: Data Engineers manage financial risks, detect fraudulent activities, and develop algorithms for automated trading. On the customer side, they analyze customer data for personalized marketing and improved retention.

What Makes a Good Data Engineer?

A good data engineer possesses a strong foundation in programming languages like Python, Java, or Scala. They also need understanding and experience with databases such as SQL and NoSQL. Familiarity with systems like Apache Spark and cloud computing platforms are a significant bonus.

But it’s not all about the technical skills. Hiring professionals looking to fill a data engineer role should prioritize problem-solving skills and a demonstrable analytical mindset. Data engineers must be able to understand complex datasets, identify patterns, and be flexible in their solutions. 

They should also have exceptional attention to detail and the ability to collaborate with diverse teams, both technical and non-technical. Data engineers frequently have to explain their work to business stakeholders in addition to data scientists and analysts. They should be able to use clear, concise language and convey meaning without getting bogged down by jargon.

It’s also a job where you’re never done learning. A commitment to continuous professional development has to be a part of the role. Technologies, tools, and best practices change frequently and data engineers have to stay ahead of the curve. Data engineers who are also agile-trained have a valuable combination of skills that help them stay ahead of the curve.

Jobsity in Action: Data Engineer Luis

Who better to explain the role of a data engineer than a data engineer himself? We sat down with one of Jobsity’s top data engineers, Luis, for a Jobsity in Action Interview:

As you can see, it’s much more than a skillset or list of attributes. You need a dev who’s excited to be a part of the team and able to commit to seeing a project through, end-to-end. You need a data engineer who’s in it for the long haul, able to take the time to learn your organization inside and out to make the best choices. 

That’s Where Jobsity Comes In

Our approach to staffing helps you level up your tech team, with long-term payoff. We find your ideal candidate, on your timeline, in your budget. We pride ourselves on taking the hassle out of hiring.

Jobsity devs stand head and shoulders above the rest, with an average retention rate of over 3 years. They represent the top 3% of LATAM talent, specializing in programming languages such as Python, Java, and Scala.  

That’s why companies like McGraw Hill and Creed Interactive trust Jobsity to provide the talent they need to make their projects a breeze.

Want a risk-free trial? Book a call today.

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Written by Donna Kmetz

Donna Kmetz is a business writer with a background in Healthcare, Education, and Linguistics. Her work has included SEO optimization for diverse industries, specialty course creation, and RFP/grant development. Donna is currently the Staff Writer at Jobsity, where she creates compelling content to educate readers and drive the company brand.

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