State Street Interview Guide Overview

About State Street

State Street Corporation is one of the largest custodian banks, asset managers, and asset intelligence companies globally. With over two centuries of history, State Street has been pioneering the financial services industry by providing investment servicing, data & analytics, investment research & trading, and investment management to institutional clients.

About the Data Engineer Position

At State Street, the Data Engineer role is critical to driving the digital transformation and expanding the company's business capabilities. Successful candidates will engage in developing scalable data pipelines, implementing cloud migration strategies, and executing real-time data processing tasks. The role demands proficiency in big data technologies like Databricks, Hadoop, Spark, and cloud platforms including Azure and AWS. Applicants should be adept at both ETL development and advanced data orchestration, ensuring data integrity and quality.

Use Interview Query to navigate through the interview process, prepare for your interview rounds effectively, and understand what it takes to succeed at State Street.

Ready to step into a challenging and rewarding role? 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 State Street as a Data Engineer. Whether you were contacted by a State Street 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 State Street 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 State Street 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 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 State Street 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 State Street’s data systems, ETL pipelines, and SQL queries.

In the case of data engineer roles, take-home assignments regarding data integration, data transformations, and code optimization might be incorporated. Your proficiency against handling large datasets, implementing data models, and cloud services 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

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 State Street 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 State Street.

Quick Tips for State Street Data Engineer Interviews

To ensure your success, consider the following tips based on previous interview experiences:

• Know Your SQL and Python: Be prepared for SQL queries, Python coding exercises, and questions about your experience with both languages. Demonstrate your capability to write efficient and optimized code.

• Familiarize with Big Data Technologies: State Street utilizes Big Data technologies like Apache Spark, Hadoop, and Databricks. Brush up on these technologies and be ready to discuss your hands-on experience.

• Understanding Data Pipelines: Highlight your experience in designing and developing data pipelines. Use case scenarios, data ingestion methods, and ETL processes are crucial points you may encounter.

Typically, interviews at State Street Bank and Trust Company vary by role and team, but commonly Data Engineer interviews follow a fairly standardized process across these question topics.

• 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 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? 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 creates multiple decision trees and why it might be preferred over logistic regression for certain tasks.

• When would you use a bagging algorithm versus a boosting algorithm? Compare two machine learning algorithms and discuss scenarios where bagging is preferred over boosting, including tradeoffs.

• What type of model predicts loan approvals and how to compare credit risk models?

• Identify the type of model used for loan approval predictions.
• Describe how to compare two credit risk models over time.

• List metrics to track the success of a new credit risk model.

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

• What are the key differences between classification models and regression models? Describe the main differences between classification and regression models, including their purposes and outputs.

• What are the Z and t-tests, and when should you use each? Explain the purpose and differences between Z and t-tests, and specify scenarios for their appropriate use.

• What are the drawbacks of the given student test score data layouts, and how would you reformat them? Analyze the provided student test score datasets, identify drawbacks, suggest formatting changes for better analysis, and describe common issues in "messy" datasets.

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

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

• How would you investigate if the redesigned email campaign led to the increase in conversion rates? Given an increase in new-user to customer conversion rates after a redesigned email journey, determine how to investigate if the increase is due to the campaign or other factors.

• 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 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.

• 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.

What is the average salary for a Data Engineer at State Street?

Q: What is the interview process like for the Data Engineer position at State Street? The interview process at State Street typically consists of multiple stages, including an initial technical interview, a managerial round, and a final HR round. The questions may focus on your resume, SQL, data engineering concepts, and your experience with relevant technologies. Expect to discuss your past projects, problem-solving abilities, and technical skills.

Q: What skills are essential for the Data Engineer role at State Street? State Street values candidates with hands-on experience in large-scale data engineering, proficiency in SQL, Python, and cloud technologies such as AWS or Azure. Skills in Big Data technologies like Hadoop, Spark, and experience with ETL processes are crucial. Effective communication, problem-solving abilities, and knowledge of financial services or compliance are also highly beneficial.

Q: What is the company culture like at State Street? State Street prides itself on fostering a collaborative and inclusive environment where diverse backgrounds and perspectives are valued. The company encourages innovation, continuous learning, and offers extensive development programs to help employees reach their full potential. They also prioritize work-life balance with flexible work programs and comprehensive benefits packages.

Q: How can I prepare for an interview at State Street? To prepare for an interview at State Street, research the company and its technology stack. Review your technical skills, particularly in SQL, Python, Big Data technologies, and cloud computing. Practice common interview questions and be ready to discuss your past projects and problem-solving approaches. Utilize Interview Query to brush up on your technical interview skills.

Q: Why should I consider working at State Street? State Street offers a dynamic work environment where technology and innovation are highly valued. The company is a leader in the financial services industry, providing opportunities to work on cutting-edge projects and technologies. With competitive benefits, a focus on diversity and inclusion, and a commitment to employee growth and development, State Street is an attractive place to advance your career.

Conclusion

Landing a Data Engineer role at State Street can be a transformative career move, providing you the opportunity to engage with cutting-edge technology and complex financial systems. The interview process may range from technical deep dives into SQL, Python, and cloud platforms, to management and leadership discussions, reflecting the comprehensive skill set required for this position. Given the variety of feedback, it's clear that preparation is key—familiarize yourself with SQL queries, cloud migration strategies, and your past project experiences.

For more insights about the company, check out our main State Street 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 State Street’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 State Street 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!