A few years ago, our Interview Query 2021 Data Science Interview Report said “data engineering is the new data science.”
Then, Data engineering was the fastest-growing job in the field. Interviews for data engineers had increased 40% year-over-year, while data science interviews grew just 10%.
Now, as the need for competent data engineers grows, more professionals and new graduates are considering careers in data engineering - and the demand is off the charts. If you’re interested in becoming a data engineer, you might be wondering exactly how to break into the field, the key skills that you’ll need, salary ranges for data engineers and what the data engineer career path looks like.
To help you out, we’ve created an overview of the data engineering career, including a description of the role, job outlook, and a look at the career ladder.
Furthermore, if you’re currently searching for a job in data Engineering, feel free to use the job board showing recent openings and company interview guides, practice with the database design interview questions, and explore the Data Engineering Learning Path.
Simply put, the job of a data engineer is to build systems that collect, process, and store data. A data engineer, for example, might build a data pipeline that collects raw data from multiple sources, like CRM and sales data, transforms it into clean, reliable information, and ultimately delivers that data to end-users like data scientists and analysts.
The data engineer plays a key role for businesses. They’re responsible for building and maintaining the data infrastructure that analysts and data scientists use to identify insights that drive business value, e.g., sales growth, operation efficiency, and other key business KPIs.
The process by which data engineers create pipelines is called ETL, or extract, transform and load. Essentially, this means extracting raw data from multiple sources, organizing and cleaning it into reliable information, and making that information accessible to data scientists and analysts. The ETL framework looks like this:
The data engineer’s role varies by company size and by industry. Larger companies and industries like finance, tech, and retail tend to have more complex data ecosystems, requiring engineers to build sophisticated data applications and pipelines.
Smaller companies, on the other hand, smaller companies typically require data engineers to manage the entire data ecosystem and provide analysis, as the role tends to be closer to that of a full-stack data scientist.
Regardless of the size of the company or industry, the data engineer’s role is to build and maintain the company’s data infrastructure, including pipelines, databases, and data warehouses, and data engineers share many different day-to-day responsibilities. Some of the key duties of a data engineer include:
Salary is one reason many are interested in data engineering careers. Entry-level data engineers, on average, earn in the low six-figures, while senior and managerial-level data engineers command upwards of $200,000.
Typically, a data engineer’s salary is dependent on a few factors, including the industry, career level and location. Here is a look at average data engineer salaries by career level:
Seniority can increase the pay of a Data Engineer. Here are the base salaries of a Data Engineer grouped into 6 seniority categories.
Getting an entry-level job in data engineering isn’t an easy task. There’s fierce competition for these roles, and beginning engineers must have a strong command of programming languages and technical skills to land competitive jobs. Beyond technical skills, it’s also important for new engineers to have a firm grasp of pipeline architecture and the concepts of how and why these pipelines are built.
Many entry-level data engineers come into the field after earning a bachelor’s degree, typically in computer science, software engineering, applied math, statistics, or a related field. Technical skills are essential for the job. Entry-level candidates must have practical experience with programming languages like Python, Scala, and Java and database systems like MySQL.
Some of the most in-demand skills for data engineering candidates are:
Although tech skills are an absolute requirement, successful data engineers must also possess so-called “soft skills,” which include collaboration, teamwork, communication, and problem-solving.
For recent graduates, a solid data engineer resume is helpful for getting your foot in the door. A well-crafted resume conveys your tech skills, your experience, and also how well you work with others.
One thing to consider: Hiring managers aren’t just looking for data engineers with technical chops; they also want candidates that understand data’s relationship to the business. Similarly, it’s important to brush up on your interviewing skills. A strong interview will set you apart and help you land your first job or internship in data engineering.
In other words, it’s important that you can answer technical data engineer interview questions but also how data pipelines can help drive business value, like operational efficiency and sales growth.
The data engineer career path is a fulfilling one, offering a chance to design and build data applications. As it’s one of the most in-demand jobs in tech right now, it’s a fiercely competitive field to enter. Candidates need more than just tech skills. They require a firm understanding of how data and pipelines drive business value.
New data engineers start in entry-level or internship positions. When just starting out, the data engineer’s role typically focuses on smaller, ad-hoc projects. As the career progresses, though, the engineer starts to take a more hands-on role in planning and strategy, with greater responsibility in the overall architecture of the data pipeline.
As a first foray into data engineering, the job description usually revolves around bug fixing and small task-oriented projects. The junior engineer’s job usually isn’t to build and scale an entire pipeline, but rather to maintain data infrastructure. Working under a senior data engineer, a junior engineer works on projects like debugging, object-oriented programming and adding smaller features.
There’s plenty of on-the-job learning as well. Most new data engineers have limited coding experience or have limited experience on the data side of coding. Therefore, your goal should be mastering core skills like coding and troubleshooting, while gaining experience with advanced coding, data design and building pipelines. Typically, this career stage lasts the first 1-3 years.
As the career progresses, data engineers still play a task-oriented role, working on ad-hoc requests. Yet, mid-level data engineers begin to also take a more proactive, project management role at this stage. The mid-level data engineer begins to collaborate more with various departments, working more closely with product managers and data scientists to design and build business- and product-oriented solutions.
At the mid-level – in years 3-5 – data engineers tend to find what they enjoy most, and develop their skills and specializations at this time.
Senior engineers take a more hands-on role in building and maintaining data collection systems and pipelines. This role typically requires much more cross-functional collaboration, with the data science and analytics teams, to develop pipelines that are tuned for deeper learning and analysis.
Senior data engineers can also take on a more managerial role, overseeing the junior engineering teams and assigning ad-hoc projects. Strategy is a key job function at the senior level. The senior engineer is tasked with defining data requirements, building and maintaining optimized pipelines and road-mapping data initiatives.
Many data engineers are content in the senior-level technical role, continuing to design and build data infrastructure. But there are many options for those looking for something new. Senior-level data engineers might transition to:
1. Chief Data Officer – This executive-level role takes responsibility for data across an entire company, creating strategy, overseeing data governance and taking a more proactive role in analysis and business intelligence. This is a more business-focused role, with the goal of aligning data to business strategy.
2. Manager of Data Engineering – Many senior-level engineers transition to a more managerial role, overseeing a company’s data engineering department. This job is part-developmental, part-managerial. Managers provide leadership to a team of data engineers, helping to coach, vet and drive the vision of the department. Managers also focus on growing the data engineering team, taking a proactive role in hiring decisions, mentoring and overseeing performance.
3. Data Architect – Data architects work closely with data engineers. Essentially, the data architect provides the blueprint to the engineering department, which is a roadmap for building advanced data models and pipelines. This is a business-oriented role: Data architects have a strong grasp of the business’s direction and strategy and help design pipelines that are aligned to specific needs.
4. Data Science Engineer – Sometimes called a full-stack data scientist, the data science engineer is a hybrid role: Part engineer, part scientist. A full-stack data scientist builds and maintains pipelines while also ensuring that actionable business insights can be derived from the model.
If you’d like to land a job in Data Engineering, Interview Query offers the following resources: