Providence is a comprehensive health care organization dedicated to serving the most vulnerable members of the community through a wide range of health services across multiple states.

As a Data Engineer at Providence, you will be responsible for designing and building modern data-centric software applications that support clinical and operational processes within the healthcare system. Key responsibilities include developing data pipelines, data transformations, and user interfaces to meet the needs of various initiatives, all while leveraging cloud computing, big data technologies, and data science methodologies. Successful candidates will possess a robust understanding of data warehousing, database design, and experience with both structured and unstructured data formats. Collaboration is emphasized, as you will work closely with product managers, developers, and other stakeholders to deliver solutions that are reliable and secure. A great fit for this role will have a passion for mentoring peers, an openness to sharing knowledge, and a commitment to maintaining high standards in code quality and documentation.

This guide will help you prepare for your interview by providing insights into the specific skills and experiences that Providence values in a Data Engineer, ensuring you present yourself as a strong candidate for the role.

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Providence as a data engineer. Whether you were contacted by a Providence 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 Providence Talent Acquisition Team will make contact and verify key details, such as your experiences and skill level. Behavioral questions may also be part of the screening process.

In some cases, the Providence data engineer hiring manager may join during the screening to answer your queries about the role and the company itself. They might also engage in surface-level technical and behavioral discussions.

The whole recruiter call should take about 30 minutes.

Online Assessment (OA)

Once you clear the initial screening, you will be required to complete an online test. The questions usually cover a range of technical topics like SCD type and type 2 design, indirect file loading, and dimension and fact table load details.

Technical Virtual Interview

Successfully navigating the OA will present you with an invitation for the technical screening round. Technical screening for the Providence data engineer role usually is conducted through virtual means, including video conference and screen sharing. Questions in this stage may revolve around data pipelines, ETL processes, the design of data warehouses, and your proficiency in SQL.

Expect questions on DBMS, OOPs, OS, data structure, and algorithms. You may also face case studies or scenarios such as building data pipelines or transforming data for clinical and operational processes.

Managerial Round

Following the technical round, a managerial round may follow. During this stage, expect questions regarding project management, your experience working in teams, and how you handle real-world data engineering challenges. This round might include some technical questions but focuses mainly on your managerial and soft skills.

HR Round

Finally, you'll have an HR round where the discussion would revolve around your fit with Providence’s work culture and values. Behavioral questions will often focus on how you handle workplace challenges and your aspirations.

Quick Tips for Providence Data Engineer Interviews

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

Tips for acing your Providence Data Engineer interview:

• Strong Conceptual Knowledge: A solid understanding of core concepts such as data pipelines, data warehousing, and ETL processes is crucial. Make sure you are prepared to discuss these in depth.
• Technical Readiness: Brush up on DBMS, OOPs, OS, data structures, and algorithms. It's also highly beneficial to be familiar with cloud computing and big data tools.
• Project Context: Be ready to discuss your past projects in detail, especially those related to data-centric applications, as interviews often emphasize real-world project experience.

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

• How does random forest generate the forest and why use it over logistic regression? Explain the process of how random forest generates multiple decision trees to form a forest. 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 two machine learning algorithms. Describe scenarios where bagging is preferred over boosting and vice versa. Provide examples of the tradeoffs between the two methods.

• 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.
• Explain how to measure the difference between two credit risk models over a timeframe, considering monthly installments.

• List the metrics to track for measuring the success of the new model.

• What’s the difference between Lasso and Ridge Regression? Describe the key differences between Lasso and Ridge Regression, focusing on their regularization techniques and impact on model coefficients.

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

• 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 the keys value and next. 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 and without punctuation.

• 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 datasets, and how would you reformat them? Assume you have data on student test scores in two layouts. 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 a subscription 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 since January 1st?

• How would you explain a p-value to a non-technical person? Explain what a p-value is in simple terms to someone who is not technical.

• What are Z and t-tests, and when should you use each? Describe what Z and t-tests are, their uses, differences, and when to use one over the other.

• 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.

• How would you reformat student test score data for better analysis? Given two datasets of student test scores, identify drawbacks in their current organization. Suggest formatting changes and discuss common issues in "messy" datasets.

• What metrics would you use to evaluate the value of marketing channels? Given data on marketing channels and costs for a B2B analytics dashboard company, identify key metrics to determine each channel's value.

• How would you determine the next partner card using customer spending data? With access to customer spending data, outline the process to identify the best partner for a new credit card offering.

• How would you investigate if a redesigned email campaign led to increased conversion rates? Analyze the impact of a redesigned email journey on conversion rates, considering other potential factors that could influence the observed increase.

If you want more insights about the company, check out our main Providence 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 Providence’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 Providence data engineer 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!