Fidelity Investments Data Engineer Interview Questions + Guide in 2024

Fidelity Investments is a leading financial services firm dedicated to making financial expertise broadly accessible and effective in helping people achieve their life goals.

Fidelity serves a diverse client base, including individual investors, businesses, and financial advisors. It offers a wide range of products and services, including investment management, retirement planning, and brokerage services.

The Data Engineer role at Fidelity will involve you in sophisticated data conversion projects, developing complex ETL pipelines, and working with cutting-edge cloud technologies. Expect to utilize your skills in SQL, Python, and cloud platforms like AWS and Azure while collaborating with various teams to deliver innovative data solutions.

In this guide, we’ll walk you through the role’s interview process, commonly asked Fidelity Investments data engineer interview questions, and expert tips to help you prepare better. Let’s get started!

Recruiter/Hiring Manager Call Screening

If your CV is among the shortlisted few, a recruiter from the Fidelity Talent Acquisition Team will contact you, often through LinkedIn, to verify key details like your experiences and skill level. Behavioral questions may also be part of the screening process.

Sometimes, the Data Engineer hiring manager may be 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 invite you to the technical screening round. This interview, usually conducted virtually, involves video conferencing and screen sharing with the hiring manager. Questions in this 1-hour long interview stage may revolve around Object-Oriented Programming (OOP) concepts, SQL queries (including joins), and data systems at Fidelity.

Case studies or take-home assignments regarding data engineering tasks such as ETL pipelines, data validation, and data migration might also be part of the evaluation. Proficiency in SAS, Python, Azure, and AWS, especially in moving data from on-prem to the cloud, will be assessed during this round.

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 will be conducted during your day at the Fidelity office, varying with the role. Your technical prowess, including programming and data engineering capabilities, will be evaluated across these interviews.

If you were assigned take-home exercises, you might be invited to a presentation round during the on-site interview for the Data Engineer role at Fidelity.

Typically, interviews at Fidelity vary by role and team, but commonly data engineer interviews follow a fairly standardized process across these question topics.

1. Create a function can_shift to determine if string A can be shifted to become string B.

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.

2. Develop a function str_map to determine if a one-to-one correspondence exists between characters of two strings at the same positions.

Given two strings, string1 and string2, write a function str_map to determine if there exists a one-to-one correspondence (bijection) between the characters of string1 and string2.

3. Write a function to determine if a string is a palindrome.

Given a string, write a function to determine if it is a palindrome. A palindrome reads the same forwards and backwards (e.g., ‘reviver’, ‘madam’, ‘deified’, ‘civic’).

4. Create a function to find the index where the sum of the left half equals the right half in a list.

Given a list of integers, find the index at which the sum of the left half of the list is equal to the right half. If no such index exists, return -1. The number at the index is included on the left side of the list.

4. 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 and aggregates their results. Discuss the advantages of using random forest over logistic regression, such as handling non-linear data and reducing overfitting.

5. When would you use a bagging algorithm versus a boosting algorithm?

Compare the scenarios where bagging and boosting algorithms are appropriate. Provide examples of the tradeoffs, such as bagging reducing variance and boosting reducing bias.

6. 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 the metrics to track for measuring the success of the new model, such as accuracy, precision, recall, and AUC-ROC.

7. 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.

8. What are the key differences between classification models and regression models?

Describe the fundamental differences between classification and regression models, including their objectives, output types, and common use cases.

9. 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.

10. 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.

11. 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 the value of each marketing channel.

12. How would you investigate whether a redesigned email campaign increased conversion rates?

Analyze a scenario where a new email campaign coincides with an increase in conversion rates. Determine if the increase is due to the campaign or other factors.

13. 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?

14. 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?

15. 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?

It’s best to brush up on technical skills and try as many practice interview questions and mock interviews as possible. A few tips for acing your Fidelity Investments interview include:

• Understand Fidelity’s Data Systems: Questions might involve detailed discussions of your experience with specific data platforms (like Snowflake, AWS, Azure) and tools (Informatica PowerCenter, Control-M). Ensure you can explain your hands-on experience in these areas.

• Proficiency in Core Technologies: Brush up on SQL (DML and DDL operations), Python, SAS, and data migration techniques. As a Data Engineer, you must be adept at these core competencies. To enhance your technical and communication skills, we recommend trying our AI interviewer.

• Collaborative and Leadership Skills: Fidelity values teamwork and leadership. Be prepared to discuss previous projects where you’ve provided technical leadership, mentored junior team members, or collaborated extensively with other teams.

What is the average salary for a Data Engineer at Fidelity Investments?

What skills and qualifications are required for a Data Engineer role at Fidelity Investments?

Candidates are expected to have a Bachelor’s or Master’s degree in a related field, such as Computer Science or Information Technology. Experience with data engineering tools like SAS, Python, SQL, and cloud platforms like Azure and AWS is essential. Experience with ETL/ELT processes, data modeling, and relational databases like Oracle is highly desirable. Demonstrated expertise in automating data pipelines and developing SAS programs is also needed.

What is the working model at Fidelity Investments?

Fidelity operates a hybrid working model blending remote work with in-person collaboration. Most hybrid roles require associates to work onsite for all business days of one assigned week per four-week period. This requirement will increase to two full assigned weeks starting in September 2024.

If you want to join a dynamic team committed to transforming data engineering practices, Fidelity Investments might just be your next home.

Curious to know more about Fidelity Investments’ interview process? Dive into our comprehensive Fidelity Interview Guide on Interview Query. You’ll find valuable insights and many interview questions to prepare you for success. We’ve also curated guides for other roles like software engineer and data analyst to help you align your skills with Fidelity’s hiring expectations.

At Interview Query, we are dedicated to empowering you with the right tools and knowledge to conquer your interviews at Fidelity Investments. Leverage our resources to sharpen your preparation and stand out in your interviews.

Good luck with your interview!