The data engineering learning path is designed to help you practice interview questions that cover key data engineering skills.

However, interviewers will also usually ask about your prior experience and familiarity with the specific tools they use. Showcasing this familiarity in your answers can be a great way to highlight your strengths and the value you’ll bring to the company.

Therefore, outside of this learning path, you can continue to optimize your chances of landing a position by:

• Keeping up with new technologies & updating your tech stack.
• Familiarizing yourself with useful programming languages.
• Conducting mock interviews to become familiar with the interview evaluation process and gain more confidence.

Updating Your Tech Stack

In the fast-paced field of tech, one of the best qualities a data engineer can have is a passion for learning, especially for new standards, frameworks, and technology.

Let’s use an extreme example in this instance. Suppose that Data Engineer A has five years of work experience while Data Engineer B has only three years. However, Engineer A has used only up to Python 2, while Engineer B is up-to-date with Python’s newest features and libraries. Who would be the more attractive engineer to hire?

While Engineer A has more years of experience, their skills could be geared more to an older standard that’s hardly used, making all of it moot. To be a more attractive hire, keep up-to-date with new technologies (e.g. the Apache tech stack, new releases by Amazon AWS, etc).

One of the core criteria in evaluating a candidate’s employability is looking at their tech stack. While it’s not necessary to match your tech stack with the company you want to work at (especially since many organizations use internal tools), being familiar with related technologies can provide assurance of your ability to adapt and use similar tech.

Specifically, gaining experience with database management systems can be really helpful for addressing many of these goals, especially if you can showcase a project with a DBMS that’s already used by the team you’re looking to join.

Review Your Programming Languages

In addition to SQL and Python, there are other popular programming languages for data engineers. These may be helpful for increasing efficiency as new standards and methods are introduced. Some popular languages to consider are:

• Query languages (including NoSQL languages such as MQL, GQL)
• Java (used heavily in Apache products with frameworks for data processing)
• R (for data visualization and statistical analysis)
• Scala (used by Spark and Databricks frameworks)

Interview Preparation Methods

Research The Company

When preparing for a job interview, it’s important to know what to expect throughout the process. While most companies follow a standard format of technical screens, behavioral interviews, whiteboard interviews, etc., the types of questions they tend to ask can vary.

For example, a fintech company might be more particular about data security, so updating your knowledge of basic industry standards on secure data handling would be important.

Before an interview, gather all the information you can about the company you seek to join. If you can, learn about the specific team you’ll be joining.

• How do they manage data in their industry?
• What’s the scope of the data engineering team you’ll be joining?
• What technologies do they use?

Any knowledge you gain will give you an idea of what questions they will ask you - and it will also help you build your case during the interview. Showcasing knowledge related to the specifics of what they do increases the chances that they’ll think you’re a good fit.

Mock Interviews

Let’s say you’ve practiced most of the technical skills required for the job and are up-to-date with the latest technologies. You prepared for weeks, but when the interview day arrived, you were caught off guard.

• You had to code on a whiteboard (or Google Docs) without your usual feedback from an IDE for minor errors.
• The time constraints made you feel pressured, causing you to rush your answers and not provide as much detail as you would have liked.
• When asked about your previous experience, you struggled to answer in a way that truly reflected your skills.

You knew the answers but weren’t familiar with how interviews work. This made you nervous and led to an unsuccessful interview.

This happens a lot. Being aware of the specifics of interview evaluation can help you feel more confident and perform better, even if you have the required technical skills.

This is where mock interviews come into play. Mock interviews are particularly great because they factor in time limit constraints and practice for other non-technical skills, such as translating an interviewer’s social cues into context to guide your approach to the problem.

Working with a peer or instructor who can create follow-up questions, catch overlooked edge cases, and help brainstorm ideal approaches will be invaluable for your interview preparation.

Mock interviews help you practice challenging questions beyond typical algorithmic problems found in test banks. Data engineering interviews often focus on designing solutions that fulfill clients’ requirements, which aren’t always present in algorithms questions.

• Familiarize yourself with whiteboard coding by using document-editing software like Google Docs or a basic text editor like Notepad.
• Don’t overlook the behavioral portion of the interview, as it assesses your soft skills and fit with the company. Be prepared to answer questions about how you handled tasks or problems in the past, as well as hypothetical workplace scenarios.
• Review your previous work experiences, especially your leadership roles and contributions, as they will likely ask you about them during the interview. Your responses should align with what’s best for the organization and its stakeholders.