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What Is a Data Engineer?

What is a data engineer? What does a data engineer do?

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A data engineer is a technology professional who builds storage solutions for vast amounts of data. These engineers are also responsible for creating the pipeline this data travels through: from input, to storage, to transformation into usable information. The need for reliable data storage and transformation is growing daily, so data engineers are becoming vital in many industries. 

In this guide, we’ll go over:

What Does a Data Engineer Do?

Many tasks can fall under the responsibility of a data engineer, but the job centers around the issue of how we acquire, store, and use data. Dushyant Sengar, director of data science at BDO USA, describes the three primary duties of a data engineer: 

  1. Designing and building data warehouses, databases, or data lakes 
  2. Monitoring and troubleshooting ETL (extract, transform, load) pipelines
  3. Database administrative tasks to manage workloads, optimization, and scaling of the data storage

A data engineer needs to build a warehouse to store data. But they also need to put in place a system where data can travel easily from the capture point (user input on a website form, for example) to the warehouse and then through transformation processes to become useful information. Once the data is usable, data scientists and analysts can use it for research, decision-making, and development. 

“As more and more companies look to use their data to give them a competitive advantage, they need experts in building scalable pipelines that can manage that data and ensure that it is of high quality,” says David Scroggins, director of software engineering for 84.51°. 

>>MORE: Learn more about data engineering.  

A Typical Day as a Data Engineer

While the overarching goal of data engineers is to build storage solutions and carry data from collection points to a usable form, the day-to-day is more focused on problem-solving.  

“Most of the day (70% or more) is spent writing code to solve a particular problem,” says Scroggins.

The problems range from finding ways to make cleaner, more accurate data, to fixing large parts of the pipeline that may have broken or become outdated. These data warehouses and pipelines need constant maintenance and upkeep, especially for highly sensitive data that could be devastating if lost, like patient information in health care.  

When data engineers aren’t focusing on one problem, they are likely still writing code. For example, a data engineer may spend a good amount of their day “building a new feature to provide new data for a customer, enhancing existing code to improve functionality, or even patching a bug to fix a production system,” says Scroggins.

While data engineering (and coding, in general) may sound like a very solitary task, there are a lot of opportunities for teamwork and client interaction in this career. 

“There is a lot of client/stakeholder interaction involved since data engineers are either helping get the right data over to them or building data warehouses/lakes based on client specifications,” says Sengar. “Also since it is a multidisciplinary environment with a range of programming and troubleshooting needed, there is bound to be a lot of teamwork.”

>>MORE: Learn how to design databases with Walmart’s Global Tech Advanced Software Engineering Virtual Experience Program.

Types of Data Engineer Jobs

A data engineer’s job generally involves the same tasks, regardless of where they work, but different types of data require different approaches. 

“Data engineers can specialize in many different tools and techniques that correspond to various types of data such as relational, graph, big data, etc.,” says Sengar.

The type of data an engineer works with depends largely on the industry. For example, health care data needs to be handled differently than retail or credit card data because of privacy and security laws. 

Big data — data sets that are too large to be handled by simple processing systems — is an emerging area desperate for data engineers. These data sets have the potential to be incredibly useful in analysis and data-driven decision-making, but the size of big data makes acquisition, storage, and sorting difficult. 

Data engineers can also specialize in specific areas. For example, a data engineer can focus entirely on developing warehouses or machine learning to make finding patterns in data easier. These specializations are often reflected in engineers’ job titles, such as:

  • Big data architect
  • Solutions architect
  • Machine learning architect
  • Technical architect
  • Data warehouse developer
  • Business intelligence developer

>>MORE: Learn more about machine learning and using Python with Standard Bank’s Data Science Virtual Experience Program.

Data Engineer Salaries

A data engineer’s salary largely depends on their experience level, location, and industry. According to Glassdoor, the overall average salary in the U.S. for data engineers is around $113,000. However, those in the early stages of their career, with up to one year of experience, may have an average annual salary of around $93,000. 

Estimates from Indeed are similar, too, with the average reported salary across all experience levels, locations, and industries being around $135,000. 

Ultimately, Payscale gives a range of $67,000 to $134,000 for base salary — those early in their career or in areas of the country with a lower cost of living may see the lower end of the range. However, more experienced engineers and those in big cities or big tech companies could get the upper end of the spectrum. 

Data engineers may also receive additional compensation annually through bonuses and stock shares. 

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Pros and Cons of Data Engineer Careers

Many opportunitiesRequires a range of skills
In-demand careerCan be monotonous
Cutting-edge Data is growing faster than skills

One of the most significant benefits of a career in data engineering is that there are so many opportunities to specialize and grow. The most exciting part, too, is that as this area of tech grows and more specializations open up, more people are getting to work on the cutting edge of data engineering, science, and technology. The exploratory nature of solving the big problem of (getting, storing, and transforming) data is perfect for people who want to use puzzle-solving skills at the forefront of tech, data, and machine learning. 

Some data engineering specializations are also incredibly in-demand. As big data gets bigger and companies continue to prioritize data-driven decision-making, data engineers who know how to handle large data sets will become more necessary. 

However, a core challenge of this career is that the data is simply growing faster than tech and skills can keep up. Finding ways to manage these massive amounts of data without the existing technology and skills is a serious struggle for data engineers and engineering teams.

“You can’t brute force your way out of data management problems,” says Scroggins. “This requires you to develop systems that can trace data quality issues across multiple interdependent systems and develop relationships with other data teams that allow you to triage problems quickly and efficiently when they arise.”

Ultimately, being a successful data engineer requires a wide range of skills. You need technical skills like SQL and Spark. However, you also need soft skills, like communication and analytical thinking, to tackle the challenges facing the data engineering industry. 

There will still be less exciting parts of the job, too. Although there is teamwork, collaboration, and problem-solving, much of the day-to-day work as a data engineer involves coding and watching existing systems to ensure they are all working properly. 

“Some maintenance jobs related to data engineering can become extremely monotonous after a while,” advises Sengar.

How to Become a Data Engineer

Education and Background 

The first step toward becoming a data engineer is usually a bachelor’s degree in something technical, like computer science, information technology, or mathematics. Technical degrees can give you the foundation in coding, problem-solving, and analytical thinking required for a successful career in data engineering. 

Some data engineers get started right out of college in entry-level roles. Students can boost their chances of finding an entry-level role by focusing on courses that teach data warehouse architecture and maintenance. 

However, it’s common for people to go into data engineering after starting in a related field. 

“Many data engineers come to the practice later in their career and first work as software or platform engineers, data scientists, or data analysts before transitioning into the role,” notes Scroggins.

Starting with a software engineering or data science background can equip data engineers with a broader skill set. This can make them more marketable and offer new insights into data engineering solutions. 

>>MORE: Explore a career in data science with BCG’s Data Science and Analytics Virtual Experience Program.

Internships, Certifications, and Bootcamps

Internships are a great way for students to break into the space and gain real experience in the field. 

Scroggins recommends students look for “internships and co-ops that allow you to spend your summers getting paid to gain experience working on a real-world project embedded in an experienced engineering team.” 

Beyond the experience internships give, they can also be opportunities to network with potential employers. “This is the best way to build experience as well as build networks that can lead to a job offer out of college,” says Scroggins of summer internship programs.

Potential data engineers also have the option to gain certification to back up their skills. Some of the most well-known tech companies (like Google and IBM) offer online certification programs. These programs teach core skills in cloud engineering, data analysis, machine learning, and data processing system design. 

The Institute for Certification of Computing Professionals (ICCP) offers a certification in data called the CDP (Certified Data Professional). This certification requires three exams, the third of which centers around a specialization. The ICCP offers various specializations for data professionals in areas like business analytics, data warehousing, data management, and information technology management. 

Additionally, bootcamps can be an excellent option for those starting in data engineering or transitioning into the career. These bootcamps are often designed for beginners and are great for learning the fundamental skills needed for this career. There are countless options for online and in-person bootcamps that focus on data, data analysis, SQL, and coding. 

>>MORE: Check out our picks for the best online coding bootcamps

Important Skills for Data Engineers

Soft Skills 

Soft skills are the non-technical skills that describe how we approach work and interact with others in the workplace. Although data engineering is highly technical, soft skills are always necessary. A vital soft skill for this career is problem-solving

“What will keep you engaged and growing in the data engineering space is a true, authentic love for solving problems and an intentional approach to understanding how to de-compose large problems into smaller, solvable problems,” says Scroggins.

Other valuable soft skills include:

  • Communication and teamwork to effectively work with clients, coworkers, and the engineering team
  • Presentation skills to explain data solutions to peers and external clients
  • Creativity and innovation to find novel solutions to complicated problems
  • Attention to detail and prioritization to know what problems to tackle first and to potentially avoid issues before they occur 

Technical and Hard Skills

The hard skill all data engineers need is SQL (search query language) — the language used to build most data engineering frameworks. 

Other technical skills all data engineers need, regardless of industry or specialization, are:

  • Understanding of programming languages like Python, JavaScript, and Scala
  • Familiarity with cloud computing, machine learning, and statistics
  • Experience using data warehouse platforms like Snowflake, Amazon’s Redshift, and IBM’s Db2 Warehouse
  • Knowledge of how to use various operating systems like macOS, Microsoft Windows, Linux, and UNIX

Start exploring your data-centric career options today with Forage’s free tech job simulations

McKayla Girardin is a NYC-based writer with Forage. She is experienced at transforming complex concepts into easily digestible articles to help anyone better understand the world we live in.