Arrow Electronics is a global provider of products, services, and solutions to industrial and commercial users of electronic components and enterprise computing solutions. Based in Centennial, CO, Arrow is renowned for its innovative approach and commitment to delivering excellence.

As a Data Engineer at Arrow Electronics, you will be responsible for designing, implementing, and maintaining production-level data pipelines and prototypes across various data sources. You will utilize cutting-edge technologies including Hadoop, Spark, Hive, Impala, and more. This role also requires technical documentation, debugging, and scaling up of prototypes to fit business needs.

If you're looking to join a company that values innovation and offers competitive financial compensation and comprehensive benefits, this guide will help you navigate the interview process for a Data Engineer position at Arrow Electronics. 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 Arrow Electronics as a Data Engineer. Whether you were contacted by an Arrow 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 Arrow 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 Arrow Data Engineer hiring manager remains 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 Arrow 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 Arrow’s data systems, ETL pipelines, and SQL queries.

In addition to coverage of data pipelines and SQL, you may face questions related to tools and technologies specific to Arrow such as Hadoop, Spark, Hive, and Impala. Your performance in scaling up ETL/ML models and services from prototypes, debugging existing workflows, and implementing production-level data pipelines will also be assessed.

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 Arrow office. Your technical prowess, including programming and ML modeling 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 Arrow.

Quick Tips For Arrow Electronics Data Engineer Interviews

• Should contain three tips for interviewing for this specific company based on interview experiences.

Example:

You should plan to brush up on any technical skills and try as many interview questions and mock interviews as possible. A few tips for acing your Arrow Electronics interview include:

• Understand Arrow Electronics' Technologies: Arrow Electronics utilizes a range of tools and technologies such as Hadoop, Cloudera, Spark, and Hive. Have a thorough understanding and practical knowledge of these technologies.
• Focus on Data Pipeline Creation: Emphasize your experience with designing and maintaining data pipelines across various data sources. Prepare to discuss your role in scaling up prototypes and ensuring product quality.
• Highlight Debugging and Monitoring Skills: Be prepared to showcase your skills in diagnosing, debugging, and fixing workflows. Discuss any previous experiences where you’ve ensured a product lives up to business SLAs through robust monitoring.

For more in-depth guides and practice resources, you can visit the Interview Query website.

Typically, interviews at Arrow Electronics 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 find the maximum number in a list of integers. Given a list of integers, write a function that returns the maximum number in the list. If the list is empty, return None.

• Create a function convert_to_bst to convert a sorted list into a balanced binary tree. Given a sorted list, create a function convert_to_bst that converts the list into a balanced binary tree. The output binary tree should be balanced, meaning the height difference between the left and right subtree of all the nodes should be at most one.

• Write a function to simulate drawing balls from a jar. Write a function to simulate drawing balls from a jar. The colors of the balls are stored in a list named jar, with corresponding counts of the balls stored in the same index in a list called n_balls.

• Develop a function can_shift to determine if one string can be shifted to become another. Given two strings A and B, write a function can_shift to return whether or not A can be shifted some number of places to get B.

• What are the drawbacks of having student test scores organized in the given layouts? Assume you have data on student test scores in two different layouts. Identify the drawbacks of these layouts and suggest formatting changes to make the data more useful for analysis. Additionally, describe common problems seen in "messy" datasets.

• How would you locate a mouse in a 4x4 grid using the fewest scans? You have a 4x4 grid with a mouse trapped in one of the cells. You can scan subsets of cells to know if the mouse is within that subset. Describe a strategy to find the mouse using the fewest number of scans.

• How would you select Dashers for Doordash deliveries in NYC and Charlotte? Doordash is launching delivery services in New York City and Charlotte. Describe the process for selecting Dashers (delivery drivers) and discuss whether the criteria for selection should be the same for both cities.

• What factors could bias Jetco's study on boarding times? Jetco, a new airline, has the fastest average boarding times according to a study. Identify potential factors that could have biased this result and explain what you would investigate further.

• How would you design an A/B test to evaluate a pricing increase for a B2B SAAS company? A B2B SAAS company wants to test different subscription pricing levels. Design a two-week-long A/B test to evaluate a pricing increase and determine whether it is a good business decision.

• How much should we budget for a $5 coupon initiative in a ride-sharing app? A ride-sharing app has a probability (p) of dispensing a $5 coupon to a rider and services (N) riders. Calculate the total budget needed for the coupon initiative.

• What is the probability of both or only one rider getting a coupon? A driver using the app picks up two passengers. Determine the probability of both riders getting the coupon and the probability that only one of them will get the coupon.

• What is a confidence interval for a statistic and why is it useful? Explain what a confidence interval is, why it is useful to know the confidence interval for a statistic, and how to calculate it.

• What is the probability that item X would be found on Amazon's website? Amazon has a warehouse system where items are located at different distribution centers. Given the probabilities that item X is available at warehouse A (0.6) and warehouse B (0.8), calculate the probability that item X would be found on Amazon's website.

• Is a coin that comes up tails 8 times out of 10 fair? You flip a coin 10 times, and it comes up tails 8 times and heads twice. Determine if this coin is fair.

• What are time series models and why are they needed? Describe what time series models are and explain why they are necessary when less complicated regression models are available.

• How would you justify the complexity of building a neural network model and explain 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 the model and explain its predictions to non-technical stakeholders?

• 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 if a decision tree is the correct model? 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 how random forest generates its forest. Additionally, why would you choose random forest over other algorithms like logistic regression?

• How would you explain linear regression to a child, a first-year college student, and a seasoned mathematician? Explain the concept of linear regression to three different audiences: a child, a first-year college student, and a seasoned mathematician. Tailor your explanations to each audience's understanding level.

• What are the key differences between classification models and regression models? Describe the main differences between classification models and regression models.

What is the average salary for a Data Engineer at Arrow Electronics?

Q: What does a Data Engineer at Arrow Electronics do?

A: A Data Engineer at Arrow Electronics designs and maintains data pipelines, prototypes software solutions, and scales up prototypes using technologies like Hadoop, Cloudera, Spark, Hive, Impala, HBase, SQL, and Python/Java. They ensure product quality, diagnose and fix workflows, and document and deploy machine learning applications.

Q: What are the salary and benefits for the Data Engineer position at Arrow Electronics?

A: The salary for this position ranges from $124,200 to $136,620 per year. Arrow Electronics offers competitive financial compensation, including medical, dental, and vision insurance, a 401k with matching contributions, paid time off, tuition reimbursement, health savings and reimbursement accounts, and short-term/long-term disability insurance, among other benefits.

Q: Can the Data Engineer position at Arrow Electronics be remote?

A: Yes, the position can work from home but the candidate must live within commuting distance of the Centennial, CO office.

Q: What kind of growth opportunities does Arrow Electronics provide?

A: Arrow Electronics supports growth opportunities through various means including financial rewards, benefits, tuition reimbursement, and continuous learning to help employees advance in their careers.

Q: How can I prepare for a Data Engineer interview at Arrow Electronics?

A: To prepare for an interview at Arrow Electronics, research the company, practice common interview questions, and review your technical skills using Interview Query. Gain familiarity with tools and technologies like Hadoop, Cloudera, Spark, Hive, Impala, HBase, SQL, and Python/Java, as these are crucial for the role.

Considering a Data Engineer role at Arrow Electronics offers an exciting opportunity to drive impactful projects across various data sources using cutting-edge technologies like Hadoop, Cloudera, Spark, and more. With a competitive salary range of $124,200 to $136,620 per year and a comprehensive benefits package, Arrow Electronics ensures both your professional growth and personal well-being. As you prepare for your interview, make sure to delve into Interview Query's extensive Arrow Electronics Interview Guide, where we cover numerous potential interview questions and offer valuable insights to help you excel. For tailored guidance, Interview Query has built a comprehensive toolkit to enhance your preparation, ensuring you approach your interview with knowledge, confidence, and strategic expertise. Good luck with your interview!