Protege is an emerging technology company renowned for its innovative solutions and dynamic presence in the tech industry. Specializing in data-driven strategies and cutting-edge technology, Protege is committed to transforming businesses through the power of data.

The Data Engineer position at Protege is a critical role, integral to the design, implementation, and maintenance of robust data pipelines and architectures. As a Data Engineer, you'll work closely with cross-functional teams to ensure the seamless flow of data, optimization of data processes, and support of data-driven decision-making.

Considering a career with Protege? You're in the right place. This guide will walk you through the Data Engineer interview process, highlight commonly asked questions, and provide insights to help you ace your interview. Let's dive in!

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Protege as a Data Engineer. Whether you were contacted by a Protege recruiter or have taken the initiative yourself, carefully review the job description and tailor your CV according to the prerequisites.

Tailoring your CV may include identifying specific keywords that the hiring manager might use to filter resumes and crafting a targeted cover letter. Furthermore, don’t forget to highlight relevant skills and mention your work experiences.

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from the Protege Talent Acquisition Team will make contact and verify key details like your experiences and skill level. Behavioral questions may also be a part of the screening process.

In some cases, the Protege Data Engineer hiring manager stays present during the screening round to answer your queries about the role and the company itself. They may also indulge in surface-level technical and behavioral discussions.

The whole recruiter call should take about 30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will present you with an invitation for the technical screening round. Technical screening for the Protege Data Engineer role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around Protege’s data systems, ETL pipelines, and SQL queries.

In the case of data engineering roles, take-home assignments regarding data transformation, storage solutions, and schema design are incorporated. Apart from these, your proficiency in writing scalable and efficient code, understanding data structures, and solving algorithmic problems may also be assessed during the round.

Depending on the seniority of the position, case studies and similar real-scenario problems may also be assigned.

Onsite Interview Rounds

Followed by a second recruiter call outlining the next stage, you’ll be invited to attend the onsite interview loop. Multiple interview rounds, varying with the role, will be conducted during your day at the Protege office. Your technical prowess, including programming and data engineering capabilities, will be evaluated against the finalized candidates throughout these interviews.

If you were assigned take-home exercises, a presentation round may also await you during the onsite interview for the Data Engineer role at Protege.

Quick Tips For Protege Data Engineer Interviews

• Brush Up on Your SQL Skills: SQL knowledge is crucial for the Data Engineer role. Make sure you’re comfortable writing and optimizing complex queries.
• Learn About ETL Pipelines: Be well-versed in designing, implementing, and managing ETL (Extract, Transform, Load) processes since these will likely be a core part of the job.
• Understand Big Data Technologies: Familiarize yourself with tools and technologies like Hadoop, Spark, and Kafka, as these are commonly used in Data Engineering roles at companies like Protege.

Typically, interviews at Protege vary by role and team, but commonly Data Engineer interviews follow a fairly standardized process across these question topics.

• Write a SQL query to select the 2nd highest salary in the engineering department. Write a SQL query to select the 2nd highest salary in the engineering department. If more than one person shares the highest salary, the query should select the next highest salary.

• Create a function precision_recall to calculate precision and recall metrics from a 2-D matrix. Given a 2-D matrix P of predicted values and actual values, write a function precision_recall to calculate precision and recall metrics. Return the ordered pair (precision, recall).

• Write a SQL query to select the top 3 departments with at least ten employees and rank them by the percentage of employees making over 100K. Given employees and departments tables, select the top 3 departments with at least ten employees and rank them according to the percentage of their employees making over 100K in salary.

• Develop a function traverse_count to determine the number of paths in an (n \times n) grid. Given an integer (n), write a function traverse_count to determine the number of paths from the top left corner of an (n \times n) grid to the bottom right. You may only move right or down.

• Create a function is_subsequence to check if one string is a subsequence of another. Given two strings, string1 and string2, write a function is_subsequence to find out if string1 is a subsequence of string2.

• - What considerations should be made when testing hundreds of hypotheses with many t-tests?
  When conducting multiple t-tests for hundreds of hypotheses, what factors should you consider to ensure the validity and reliability of your results?

• How does random forest generate the forest and why use it over logistic regression? Random forest generates a forest by creating multiple decision trees using bootstrapped subsets of the data and random subsets of features. It is often preferred over logistic regression for its ability to handle non-linear relationships and interactions between features.

• How do we deal with missing square footage data to construct a housing price model? To predict housing prices in Seattle with 20% of listings missing square footage data, you can use techniques like imputation (mean, median, or model-based), or exclude those records if the dataset is large enough.

• How would you combat overfitting when building tree-based models? To combat overfitting in tree-based models, you can use techniques such as pruning, setting a maximum depth, using a minimum number of samples per leaf, or employing ensemble methods like random forests.

• Will increasing the number of trees in a random forest always increase model accuracy? Increasing the number of trees in a random forest generally improves accuracy up to a point, but after a certain number, the gains diminish and may lead to longer training times without significant accuracy improvements.

• How would you implement the k-means clustering algorithm in Python from scratch? Given a two-dimensional NumPy array data_points, number of clusters k, and initial centroids initial_centroids, implement the k-means algorithm to return a list of cluster assignments for each data point. The algorithm involves iterating between assigning points to the nearest centroid and updating centroids based on the mean of assigned points until convergence.

• How would you explain what a p-value is to someone who is not technical? Explain the concept of a p-value in simple terms to someone without a technical background.

• How should you handle a right-skewed distribution when predicting real estate home prices? If home prices in a city are skewed to the right, should you take any action? If so, what steps should you take? Bonus: How would you handle a heavily left-skewed target distribution?

In conclusion, interviewing for a Data Engineer position at Protege offers a unique blend of technical challenges and growth opportunities. If you want more insights about the company, check out our main Protege Interview Guide, where we have covered many interview questions that could be asked. We’ve also created interview guides for other roles, such as software engineer and data analyst, where you can learn more about Protege’s interview process for different positions.

At Interview Query, we empower you to unlock your interview prowess with a comprehensive toolkit, equipping you with the knowledge, confidence, and strategic guidance to conquer every Protege interview question and challenge.

You can check out all our company interview guides for better preparation, and if you have any questions, don’t hesitate to reach out to us.

Good luck with your interview!