Coding is incredibly important in data science. Across the board, companies use data engineer coding challenges to get ahead of the competition. After all, the supply of data engineers is slimmer than the demand. To make informed skills-based hiring decisions at a fast-paced, high-level performance rate, implement data engineer coding challenges.
Data science is the study of information and mathematical evaluations. When hiring a data engineer, the candidate must possess a high level of intelligence and comprehension of the subject matter to do the job justice. Data engineer coding challenges usually involve practice problems and scenarios based on real-world issues that data engineers mitigate. These challenges are taken by recruitment teams and used to assess candidates and select the best fit for the role.
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It’s essential when creating a data engineering assessment that the data engineer coding challenges are based on realistic expectations and real scenarios that take place in the actual position. The simulation approach to hiring takes data-driven technology and integration tools to create coding challenges that provide more insight into each candidate so recruiters can make better hiring decisions faster.
It’s important to utilize analytics and data when recruiting and hiring staff. Improving these hiring decisions can reduce turnover rates and improve productivity in the long term. As recruiters are the backbone of every company's success, it’s important to provide them with the best resources and tools they need.
The first part of hiring a data engineer is to screen their resumes. Resumes provide great insight and background into each candidate before the interview and challenges. This insight can help guide recruiters during the interview process. Unfortunately, resume screening can take a lot of time. Luckily, artificial intelligence resume screening can present the same information and more than a recruiter can in a fraction of the time. The screening process can also present bias when assessed by a human. AI eliminates this bias.
Artificial intelligence resume screening doesn’t only determine technical skills in data engineers but also soft skills such as:
This automation tool helps recruiters get the information necessary for the hiring process without wasting time so they can focus on candidates.
The first part of the actual data engineer coding challenge is the take-home test. Each candidate should be sent home with a virtual test that assesses their level of proficiency in generalized data development.
Taking the information from the AI resume screening, the take-home test should include various questions to provide an overall picture of the job requirements. Multiple-choice questions, while sometimes necessary, are not always the most effective. Take-home tests should include:
Each of these questions serve a separate purpose. Including all variations of data science in variations of questions can help recruiters determine whether the candidate truly understands the material.
Like resume screening, take-home tests are crucial but can take up a lot of time and resources to grade, which the hiring process does not have room for. Integrating hiring platforms seamlessly with your previous ATS and including the support of automatic grading can help tremendously.
The live-coding assessment part of the interview will also incorporate a data engineer coding challenge. Although, this aspect of the interview will be reserved for candidates who truly meet the mark for the position.
When providing a live-coding assessment, it’s important to provide virtual environments to each candidate. These environments should be able to mimic your company’s real virtual environments, so then the interview is as close to the real world as possible. It’s often seen in data engineering interviews where data engineers code in any integrated development environment (IDE) of their choice. This is a bad practice because it leaves room for error. Some IDEs provide exceptional functionality and sophisticated features to help developers during their work, sometimes, these require a subscription of some sort to access. Other IDEs are pretty basic and don’t include all the extra features. If each candidate utilizes a different IDE then the base for the results will be drastically different. These provided features can skew the results.
This would also not encourage inclusive practice if the data scientists that show up on top are subscribing to the IDEs that have the better features. After all, the best candidate may not come from a background of privilege. Potential in an employee is much more important than pedigree.
Virtual whiteboards are also beneficial in live-coding assessments as they provide candidates with the opportunity to visualize their thought processes to the recruiters. Data engineers are also going to need to build diagrams over time. The whiteboard can showcase diagrams like data flow diagrams (DFDs).
Finding data engineer coding challenges, and the data-driven technology to go along with the interview can be difficult. After all, the best data engineers most likely have multiple opportunities on the table. To get ahead of the competition, your recruitment team must have overall professionalism to them while gathering the information necessary, all with a quick turnaround time. Hiring platforms like Filtered can provide the analytics, insights, tools, and data engineer coding challenges for you!
Filtered is a leader in skills-based hiring. Our end-to-end technical hiring platform enables you to spend time reviewing only the most qualified candidates, putting skills and aptitude at the forefront of your decisions. We’ll help you automate hiring while applying objective, data-driven techniques to consistently and confidently select the right candidates. To get started, contact our team today or register for a FREE demo.
