S&P Global Data Engineer Interview Questions + Guide in 2024

S&P Global is a leading provider of credit ratings, benchmarks, analytics, and workflow solutions in the global capital, commodity, and automotive markets. They help the world’s leading organizations navigate economic landscapes, facilitating confident decision-making.

Explore this guide for a comprehensive walkthrough of the interview process, key topics, and potential S&P Global data engineer interview questions to prepare you for your journey.

The interview process usually depends on the role and seniority, however, you can expect the following on an S&P Global data engineer interview:

Recruiter/Hiring Manager Call Screening

Should your CV pass the initial screening, a recruiter from S&P Global will get in touch to verify key details, such as your work history and skill level. This call may also include some behavioral questions to gauge your cultural fit with the company.

In certain cases, the Data Engineering hiring manager may also be present during the screening call to further discuss the role and answer any questions you may have. They might delve into surface-level technical and behavioral topics as well.

The recruiter screening call usually lasts around 30 minutes.

Technical Virtual Interview

Successful candidates from the initial screening will be invited for a technical virtual interview. This stage typically involves video conferencing and screen sharing tools. The interview, which usually lasts around an hour, can cover a range of topics crucial for the Data Engineer role at S&P Global.

Expect questions about Python programming, ETL pipelines, SQL queries, and data manipulation. You may also be given problems related to automation, machine learning, and data engineering processes to solve in real-time.

Depending on the role, you might also receive a take-home assignment focusing on data extraction, transformation, and automation of data processes.

Onsite Interview Rounds

After clearing the technical virtual interview, you’ll move on to the onsite interview loop, which often involves multiple rounds. During these rounds, you will engage with various team members and stakeholders who will evaluate your technical skills in-depth, including coding and systems design.

You will also have the opportunity to demonstrate your problem-solving abilities through practical exercises or whiteboard sessions. If a take-home assignment was given in the prior stage, you might need to present your solution as part of the interview.

Final Interview

The final interview round, often with senior leadership or team leads, aims to assess your strategic thinking, long-term career goals, and cultural fit. By this point, the focus shifts more towards your alignment with S&P Global’s mission, values, and how you might contribute to upcoming projects within the Data Engineering team.

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

1. 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.

2. 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.

3. 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.

4. How would you determine the next partner card using customer spending data?

With access to customer spending data, outline a method to identify the best partner for a new credit card offering.

5. 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 how to verify if the campaign caused the increase or if other factors were involved.

6. How would you use linear interpolation to fill missing temperature data in a time-series data frame?

A climate research organization has a time-series data frame with daily city temperature readings. Some days have missing temperature readings. Write a Python function using Pandas to perform linear interpolation to estimate and fill the missing data. Ensure the interpolation considers data from the same city only. Assume no consecutive missing data and valid temperature data at the start and end dates.

7. 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.

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

Compare two machine learning algorithms. Describe scenarios where bagging (e.g., random forest) is preferred for reducing variance and boosting (e.g., AdaBoost) is preferred for reducing bias. Provide examples of tradeoffs between the two.

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

1. Identify the type of model developed by the co-worker for loan approval.
2. Describe how to measure the difference between two credit risk models over a timeframe, considering monthly installment payments.
3. List metrics to track the new model’s success, such as accuracy, precision, recall, and AUC-ROC.

10. What’s the difference between Lasso and Ridge Regression?

Explain the differences between Lasso and Ridge Regression, focusing on their regularization techniques. Highlight how Lasso performs feature selection by shrinking coefficients to zero, while Ridge penalizes large coefficients without eliminating features.

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

Describe the primary differences between classification and regression models. Classification models predict categorical outcomes, while regression models predict continuous outcomes. Discuss their use cases and evaluation metrics.

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 S&P Global data engineer interview include:

• Prepare for Python Programming: Python is crucial for the Data Engineer role at S&P Global. Ensure you’re proficient in it, along with libraries like Pandas and NumPy.
• Understand Data Engineering Concepts: Be well-versed with data extraction, transformation, and loading (ETL), as well as data pipeline architecture.
• Showcase Problem-Solving Skills: Demonstrate your ability to tackle complex data problems efficiently and clearly explain your process and thinking.

What is the average salary for a Data Engineer at S&P Global?

What key technologies should I be proficient in for a Data Engineer role at S&P Global?

You should have strong experience in Python and be familiar with technologies such as GitHub, DataBricks, FastAPI, Machine Learning, NLP, Data Mining, Docker, Linux, Kubernetes, and AzureDevOps. Experience with SQL databases, NoSQL databases, and cloud technologies like AWS is highly desirable.

What type of projects will I be working on as a Data Engineer at S&P Global?

You will work on high-profile initiatives such as Generative AI, Auto Document Tagging, Unstructured Data Extraction, and ML/LLM Driven Extraction. As part of your role, you will enhance data delivery, create automation solutions, and drive transformation projects using Python and other modern technologies to make data processes more efficient.

What does S&P Global value in its workplace culture for Data Engineers?

S&P Global values integrity, discovery, and partnership. The company promotes a collaborative environment where innovative thinking is encouraged. They focus on powering global markets with essential intelligence to create a more prosperous and sustainable future.

What benefits can I expect as a Data Engineer at S&P Global?

S&P Global offers comprehensive benefits, including health and wellness coverage, flexible downtime, continuous learning resources, competitive pay, retirement planning, family-friendly perks, and various small but impactful perks. They also strongly commit to diversity, equity, and inclusion in the workplace.

If you want to immerse yourself in a culture of integrity, discovery, and partnership, S&P Global is the place for you. They’re committed to fostering an inclusive environment where diverse perspectives fuel their collective success. Explore the possibilities and become part of a team dedicated to making a tangible impact on the world.

If you want more insights about the company, check out our main S&P Global Interview Guide, where we have covered many interview questions that could be asked. Additionally, explore our interview guides for other roles such as machine learning engineer and data analyst to learn more about S&P Global’s interview process for different positions.

Good luck with your interview!