Ut Health Houston, a renowned institution dedicated to fostering health and medical research, is seeking talented individuals for their Data Engineer positions. This role involves leveraging technical expertise to manage, analyze, and integrate complex data sets crucial to groundbreaking healthcare research and operations.

The interview process typically begins with a phone screen by a recruiter or HR personnel, followed by an in-person interview with the hiring manager. The final step involves delivering a short presentation to a panel. As a Data Engineer at Ut Health Houston, you will be expected to articulate your thoughts clearly and demonstrate proficiency in problem-solving, such as reversing a linked list and resolving project-related challenges.

This guide will help you navigate the Ut Health Houston interview process, providing insights and tips to excel and secure your role as a Data Engineer. 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 UT Health Houston as a Data Engineer. Whether you were contacted by a 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 UT Health Houston 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 UT Health Houston 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 entire 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 UT Health Houston 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 data systems, ETL pipelines, and SQL queries.

In the case of data engineering roles, take-home assignments regarding data warehousing, data modeling, and data transformation tasks are incorporated. Apart from these, your proficiency in programming languages such as Python or Java 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

Following 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 UT Health Houston 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 UT Health Houston.

Quick Tips For UT Health Houston Data Engineer Interviews

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

• Be Clear and Coherent: Make sure to express your thoughts clearly and coherently, as communication is crucial for this role.
• Review Core Concepts: Be prepared to answer foundational questions such as "How would you reverse a linked list?" and other basic data structure problems.
• Prepare for Behavioral Questions: Practice responses to questions like "Describe a difficult situation you have encountered with a co-worker on a project and how did you help improve the situation."

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

• What are the Z and t-tests, and when should you use each? Explain the purpose and differences between Z and t-tests. Describe scenarios where one test is preferred over the other.

• What are the drawbacks of the given student test score datasets, and how would you reformat them? Analyze the provided student test score datasets for potential issues. Suggest formatting changes to make the data more useful for analysis. Discuss common problems in "messy" datasets.

• What metrics would you use to determine the value of each marketing channel? Given data on marketing channels and their costs, identify the metrics you would use to evaluate the value of each channel.

• How would you determine the next partner card using customer spending data? Using customer spending data, outline the process to identify the most suitable partner for a new partner card.

• How would you investigate if the redesigned email campaign led to the increase in conversion rates? Given the increase in new-user to customer conversion rates, determine how to investigate whether the redesigned email campaign caused the increase or if other factors were involved.

• Write a function search_list to check if a target value is in a linked list. Write a function, search_list, that returns a boolean indicating if the target value is in the linked_list or not. You receive the head of the linked list, which is a dictionary with value and next keys. If the linked list is empty, you'll receive None.

• Write a query to find users who placed less than 3 orders or ordered less than $500 worth of product. Write a query to identify the names of users who placed less than 3 orders or ordered less than $500 worth of product. Use the transactions, users, and products tables.

• Create a function digit_accumulator to sum every digit in a string representing a floating-point number. You are given a string that represents some floating-point number. Write a function, digit_accumulator, that returns the sum of every digit in the string.

• Develop a function to parse the most frequent words used in poems. You're hired by a literary newspaper to parse the most frequent words used in poems. Poems are given as a list of strings called sentences. Return a dictionary of the frequency that words are used in the poem, processed as lowercase.

• Write a function rectangle_overlap to determine if two rectangles overlap. You are given two rectangles a and b each defined by four ordered pairs denoting their corners on the x, y plane. Write a function rectangle_overlap to determine whether or not they overlap. Return True if so, and False otherwise.

• How would you design a function to detect anomalies in univariate and bivariate datasets? If given a univariate dataset, how would you design a function to detect anomalies? What if the data is bivariate?

• What are the drawbacks of the given student test score data layouts? Assume you have data on student test scores in two layouts (dataset 1 and dataset 2). What are the drawbacks of these layouts? What formatting changes would you make for better analysis? Describe common problems in “messy” datasets.

• What is the expected churn rate in March for customers who bought subscriptions since January 1st? You noticed that 10% of customers who bought subscriptions in January 2020 canceled before February 1st. Assuming uniform new customer acquisition and a 20% month-over-month decrease in churn, what is the expected churn rate in March for all customers who bought the product since January 1st?

• How would you explain a p-value to a non-technical person? How would you explain what a p-value is to someone who is not technical?

• What are Z and t-tests, and when should you use each? What are the Z and t-tests? What are they used for? What is the difference between them? When should you use one over the other?

• How does random forest generate the forest and why use it over logistic regression? Explain how random forest generates multiple decision trees and combines their results. Discuss the advantages of using random forest over logistic regression, such as handling non-linear data and reducing overfitting.

• When would you use a bagging algorithm versus a boosting algorithm? Compare the use cases for bagging and boosting algorithms. Provide examples of tradeoffs, such as bagging reducing variance and boosting improving accuracy but being more prone to overfitting.

• How would you evaluate and compare two credit risk models for personal loans?

• Identify the type of model developed by the co-worker for loan approval.
• Describe how to measure the difference between two credit risk models over a timeframe, considering monthly installments.

• List metrics to track the success of the new model, such as accuracy, precision, recall, and AUC-ROC.

• What’s the difference between Lasso and Ridge Regression? Explain the key differences between Lasso and Ridge Regression, focusing on their regularization techniques and how they handle feature selection and multicollinearity.

• What are the key differences between classification models and regression models? Describe the main differences between classification and regression models, including their objectives, output types, and common use cases.

What is the average salary for a Data Engineer at Ut Health Houston?

Q: What is the interview process for the Data Engineer position at UT Health Houston like?
The interview process at UT Health Houston typically starts with a phone screen conducted by a recruiter or HR personnel. If you pass this stage, the second step involves an in-person interview with the hiring manager. The final step includes a short presentation to a panel.

Q: What kind of technical questions can I expect during the interview?
You can expect questions that assess your problem-solving and technical skills. Examples include algorithm-based questions like "How would you reverse a linked list?" and practical scenarios like describing how you would handle a difficult situation with a co-worker on a project.

Q: What should I focus on when preparing for the interview?
When preparing for the interview, focus on practicing common interview questions, refining your technical skills, and preparing to clearly express your thoughts. Utilizing resources like Interview Query can greatly help you prepare effectively.

Q: What makes UT Health Houston a great place to work as a Data Engineer?
UT Health Houston is a leading health institution that offers a collaborative and innovative work environment. As a Data Engineer, you get the opportunity to work on meaningful projects that can have a significant impact on healthcare outcomes.

Q: How important are soft skills for the Data Engineer position at UT Health Houston?
Soft skills are crucial for this role. The ability to clearly articulate your thoughts, collaborate with team members, and resolve conflicts efficiently is highly valued. You may be asked to describe a difficult situation you encountered with a co-worker and how you helped improve it.

If you want more insights about the company, check out our main UT Health Houston 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 UT Health Houston’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 UT Health Houston Data Engineer interview questions and challenges.

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!