Koverse, Inc., an SAIC company, empowers customers to use data to gain understanding and drive mission-impacting decisions and actions. Our technology powers game-changing solutions with unprecedented scale, security, and performance. Our team has contributed to some of the most strategic data initiatives sponsored by government organizations and global industry leaders.

As a Data Engineer at Koverse, you'll be a key contributor in a culture that promotes inclusivity, respect, and productivity, whether working remotely or in the office. We seek tech-savvy, curious problem-solvers who enjoy tackling complex issues in a fast-paced, evolving industry. You will work directly with customers to ensure smooth adoption of the Koverse platform, developing cutting-edge solutions and providing comprehensive support through installation, documentation, and training.

This guide will help you navigate the interview process, commonly asked questions, and valuable tips for your Data Engineer role at Koverse. Let’s get started!

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Koverse, Inc. as a Data Engineer. Whether you were contacted by a Koverse 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 Koverse 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 Koverse 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 Koverse 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 Koverse’s data systems, ETL pipelines, and proficiency with tools like Hadoop, Spark, and Kafka.

In the case of Data Engineer roles, take-home assignments or live coding sessions may be incorporated, requiring knowledge of distributed systems, data pipeline architecture, and machine learning concepts. Apart from these, your scripting proficiency in languages like Python or Linux shell scripts will 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 Koverse office or virtually if the situation requires. Your technical prowess, including working with cloud technologies and detailed data manipulation 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 Koverse.

Quick Tips For Koverse Data Engineer Interviews

You should plan to brush up on any technical skills and try as many practice interview questions and mock interviews on Interview Query as possible.

• Understand Data Systems: Koverse’s work involves large-scale data systems including Hadoop, Spark, and Kafka. Familiarize yourself with these technologies and understand how they integrate within a data environment.
• Be Cloud Savvy: Given the emphasis on cloud technologies like AWS, Azure, and GCP, ensure you are comfortable with deploying and maintaining distributed computing infrastructures within these environments.
• Emphasize Security Clearance: Since a Top Secret or Secret Security Clearance is imperative for the role, discuss any past experiences you have had working in secure environments and handling sensitive data.

Typically, interviews at Koverse, Inc. 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.

• Write a function to merge two sorted lists into one sorted list. Given two sorted lists, write a function to merge them into one sorted list. Bonus: Determine the time complexity.

• Create a function missing_number to find the missing number in an array. You have an array of integers, nums of length n spanning 0 to n with one missing. Write a function missing_number that returns the missing number in the array. Complexity of (O(n)) required.

• Develop 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 function to search for a target value in a rotated sorted array. Given a rotated sorted array and a target value, write a function to search for the target value. If the value is in the array, return its index; otherwise, return -1. Bonus: The algorithm's runtime complexity should be in the order of (O(\log n)).

• Would you suspect anything unusual about the A/B test results with 20 variants? Your manager ran an A/B test with 20 different variants and found one significant result. Would you consider this result suspicious?

• How would you set up an A/B test to optimize button color and position for higher click-through rates? A team wants to A/B test changes in a sign-up funnel, such as changing a button from red to blue and/or moving it from the top to the bottom of the page. How would you design this test?

• What steps would you take if friend requests on Facebook are down 10%? A product manager at Facebook reports a 10% decrease in friend requests. What actions would you take to investigate and address this issue?

• Why might job applications be decreasing while job postings remain constant? You observe that the number of job postings per day has remained stable, but the number of applicants has been steadily decreasing. What could be causing this trend?

• What are the drawbacks of the given student test score datasets, and how would you reformat them for better analysis? You have data on student test scores in two different layouts. What are the drawbacks of these formats, and what changes would you make to improve their usefulness for analysis? Additionally, describe common issues in "messy" datasets.

• Is this a fair coin? You flip a coin 10 times, and it comes up tails 8 times and heads twice. Determine if the coin is fair based on this outcome.

• How do you write a function to calculate sample variance? Write a function that outputs the sample variance given a list of integers. Round the result to 2 decimal places. Example input: test_list = [6, 7, 3, 9, 10, 15]. Example output: get_variance(test_list) -> 13.89.

• Is there anything fishy about the A/B test results with 20 variants? Your manager ran an A/B test with 20 different variants and found one significant result. Would you suspect anything unusual about these results?

• How do you find the median in (O(1)) time and space for a list with a majority element? Given a list of sorted integers where more than 50% of the list is the same repeating integer, write a function to return the median value in (O(1)) computational time and space. Example input: li = [1,2,2]. Example output: median(li) -> 2.

• What are the drawbacks and formatting changes needed for messy datasets? Assume you have data on student test scores in one of the given layouts (dataset 1 and dataset 2). Identify the drawbacks of the current organization, suggest formatting changes for better analysis, and describe common problems in messy datasets.

• How would you evaluate the suitability and performance of a decision tree model for predicting loan repayment? You are tasked with building a decision tree model to predict if a borrower will repay a personal loan. How would you evaluate whether a decision tree is the correct model for this problem? If you proceed with the decision tree, how would you evaluate its performance before and after deployment?

• How does random forest generate the forest, and why use it over logistic regression? Explain the process by which a random forest generates its forest. Additionally, discuss why one might choose random forest over other algorithms such as logistic regression.

• When would you use a bagging algorithm versus a boosting algorithm? Compare two machine learning algorithms. In which scenarios would you use a bagging algorithm versus a boosting algorithm? Provide examples of the tradeoffs between the two.

• How would you justify using a neural network model and explain its predictions to non-technical stakeholders? Your manager asks you to build a neural network model to solve a business problem. How would you justify the complexity of this model and explain its predictions to non-technical stakeholders?

• What metrics would you use to track the accuracy and validity of a spam classifier? You are tasked with building a spam classifier for emails and have completed a V1 of the model. What metrics would you use to track the accuracy and validity of the model?

What is the average salary for a Data Engineer at Koverse, Inc.?

Q: What is Koverse, Inc.?

Koverse, Inc., an SAIC company, empowers customers to harness data to make mission-critical decisions. With a security-first platform, Koverse supports government organizations and highly regulated industries in data analytics, data science, and AI applications.

Q: What makes Koverse's work culture unique?

Koverse fosters an inclusive, respectful, and fun work environment. The culture encourages transparency, accountability, and candid discussion. Employees can work remotely or in a hybrid model and enjoy flexible schedules that promote work-life balance.

Q: What qualifications do you need to be a Data Engineer at Koverse?

Candidates must have an active Secret or Top Secret Security Clearance. Technical experience with distributed systems like Hadoop and Spark, cloud technologies such as AWS or GCP, and scripting languages like Python is essential. Excellent communication skills and a strong ability to build customer relationships are also crucial.

Q: What does a Data Engineer at Koverse do?

A Data Engineer works directly with customers to ensure smooth adoption of the Koverse platform and designs customized machine learning solutions. Responsibilities include deployment planning, system setup, customer documentation, and providing customer training.

Q: What benefits can employees expect at Koverse?

Koverse offers some of the best benefits in tech, including a flexible hybrid work model, team lunches during on-site days, medical, dental, and vision insurance, company-paid life insurance, parental leave, and unlimited personal time off. Additionally, there are opportunities for occasional travel for company-wide meetings and social events.

Interested in joining a team that drives mission-impacting decisions with unprecedented data solutions? Koverse, Inc., an SAIC company, is your place to be. We're searching for tech-savvy, curious problem-solvers who thrive in a fast-paced, evolving industry. As a Data Engineer at Koverse, you will work directly with customers and build cutting-edge solutions, requiring active Secret or Top Secret Security Clearance. If you have expertise in distributed systems, data pipelines, and cloud technologies, we want you on our team. Ready to take on the challenge? Check out our main Koverse Interview Guide on Interview Query for insights and tips on conquering your interview. Equip yourself with the knowledge, confidence, and strategic guidance to stand out. Good luck with your interview!