Urbane Systems LLC stands out as an industry leader in providing innovative and effective data solutions. We are committed to leveraging cutting-edge technology and highly skilled teams to deliver unparalleled insights and performance for our clients.

As a Senior Data Engineer at Urbane Systems, you'll play a pivotal role in designing, implementing, and automating our distributed systems for data collection and processing. You will be responsible for schema design, managing data warehouses, and developing robust metrics and reports. ETL expertise, proficiency in database systems, and a deep understanding of data pipeline construction are essential. You will also collaborate closely with business analysts, data scientists, and other internal partners to identify and resolve data-related issues and drive future data architecture plans.

This guide will walk you through the interview process, commonly asked questions, and preparation tips to help you succeed in your role at Urbane Systems. Let's get started with Interview Query!

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Urbane Systems LLC as a Senior Data Engineer. Whether you were contacted by a Urbane Systems LLC 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 Urbane Systems LLC 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 Urbane Systems LLC 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 Senior Data Engineer role at Urbane Systems LLC is usually conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around Urbane Systems LLC’s data systems, ETL pipelines, and SQL queries.

In the case of data engineering roles, take-home assignments regarding data schema design, ETL processes, and automation may be incorporated. Apart from these, your proficiency with data migration from Oracle to AWS PostgreSQL, Python, Spark, and other relevant technologies will 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 Urbane Systems LLC office. Your technical prowess, including database management and performance tuning 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 Senior Data Engineer role at Urbane Systems LLC.

Quick Tips For Urbane Systems LLC Senior Data Engineer Interviews

• Understand the Technical Scope: Make sure you are well-versed with data migration, ETL processes, and database performance tuning. Brush up on AWS, PostgreSQL, Python, and Spark.
• Brush Up on Data Schema Design: Being able to articulate the differences between various data types and models, as well as understanding schema design, will be crucial.
• Collaborative Problem Solving: Emphasize your experience working collaboratively with Business Analysts, Data Scientists, and other partners, as teamwork is a key part of the role.

Typically, interviews at Urbane Systems Llc 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 have a height difference of at most one between the left and right subtrees of all nodes.

• 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 what you would investigate further.

• How would you design an A/B test to evaluate a pricing increase for a B2B SAAS company? You work at a B2B SAAS company interested in testing different subscription pricing levels. Describe how you would design a two-week-long A/B test to evaluate a pricing increase and determine if it is a good business decision.

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

• What is the probability of both riders getting the coupon? A driver using the app picks up two passengers. Determine the probability that both riders will receive the coupon.

• What is the probability that only one of them will get the coupon? A driver using the app picks up two passengers. Determine the probability that only one of the riders will receive 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. In one city, the probability that item X is available at warehouse A is 0.6 and at warehouse B is 0.8. Calculate the probability that item X would be found on Amazon's website.

• 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.

• What are time series models and why do we need them? Describe what time series models are and explain why they are necessary when we have less complicated regression models.

• 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 this model and explain its predictions to non-technical stakeholders?

• How would you evaluate and deploy 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, and how would you assess its performance before and after deployment?

• How does random forest generate the forest, and why use it over logistic regression? Explain how a 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.

Urbane Systems LLC offers an exceptional opportunity for senior data engineers to innovate and drive business-critical decisions through advanced data engineering. You will harness your expertise in data migration, ETL processes, and automated data pipelines to build systems that process millions of data points and improve database performance. This role allows you to collaborate closely with analysts, scientists, and other partners, ensuring impactful data-driven strategies. If you're ready to advance your career and tackle exciting challenges, explore more insights about the company in our Urbane Systems LLC Interview Guide. At Interview Query, we provide the resources to help you succeed, from comprehensive interview guides to strategic preparation tools. Good luck with your interview!