What is a Data Engineer?

At S&P Global, data is not just a byproduct of operations; it is the core product. As a global leader in financial information and analytics, the company relies on the accuracy, speed, and availability of vast datasets to power credit ratings, benchmarks, and analytics for the capital markets. A Data Engineer here plays a pivotal role in maintaining the integrity of the financial ecosystem. You are not simply moving data from point A to point B; you are architecting the pipelines that deliver critical intelligence to investors, governments, and companies worldwide.

In this role, you will design, build, and optimize high-throughput data pipelines that ingest structured and unstructured data from diverse sources. You will work within complex cloud environments—primarily AWS—to ensure data quality and accessibility for downstream analytics and machine learning teams. The work requires a blend of software engineering rigor and data management expertise, often involving legacy system migrations to modern cloud architectures like Databricks or AWS Glue.

This position offers a unique opportunity to work at a massive scale where precision is non-negotiable. You will collaborate with cross-functional teams, including product managers and data scientists, to solve complex challenges related to data latency, governance, and scalability. If you are driven by the challenge of transforming raw financial data into actionable insights that drive global markets, this role provides the platform to make a tangible impact.

Common Interview Questions

These questions are based on real interview experiences from candidates who interviewed at this company. You can practice answering them interactively on Dataford to better prepare for your interview.

Getting Ready for Your Interviews

Preparing for an interview at S&P Global requires a shift in mindset. You should approach the process not just as a test of your coding skills, but as an evaluation of your ability to handle data responsibly and efficiently in a regulated environment. The interviewers are looking for engineers who understand the "why" behind their architectural choices, not just the "how."

You will be evaluated on several key criteria throughout the process:

Technical Proficiency This is the baseline. Interviewers will assess your deep understanding of SQL, Python, and distributed computing frameworks like Spark. You must demonstrate the ability to write clean, efficient code and optimize queries for performance.

Architectural Understanding Beyond coding, you need to show that you can design robust systems. You will be evaluated on your knowledge of ETL vs. ELT, data modeling (dimensional modeling, Star/Snowflake schemas), and your ability to select the right AWS services for specific use cases.

Operational Excellence S&P Global values stability. You should be ready to discuss CI/CD pipelines (specifically Jenkins), version control, and how you handle error logging, monitoring, and data quality checks in production environments.

Problem-Solving & Adaptability Data engineering often involves troubleshooting ambiguous issues. You will be tested on your ability to debug complex failures—such as Spark memory issues or pipeline bottlenecks—and your adaptability in learning new tools as the technology stack evolves.

Interview Process Overview

The interview process for a Data Engineer at S&P Global is generally comprehensive, designed to test both your foundational knowledge and your practical application of skills. However, candidates should be prepared for a process that can occasionally face administrative delays. The flow typically begins with a recruiter screen or an HR discussion regarding your background and salary expectations. It is important to be responsive and patient during this stage, as scheduling coordination can sometimes be iterative.

Following the initial screen, you will move into technical rounds. These interviews are structured to assess your hands-on capabilities. You can expect a mix of coding challenges, SQL assessments, and architectural discussions. The difficulty is generally considered "Medium," meaning the questions are standard for the industry but require solid fundamental understanding rather than obscure algorithmic tricks. The focus is often on practical scenarios you would face on the job, such as handling incremental loads or optimizing database indexes.

The process usually culminates in a series of back-to-back interviews (often virtual) covering technical depth, system design, and behavioral fit. Throughout these stages, the interviewers are looking for consistency in your technical answers and a clear demonstration of your experience with their specific tech stack (AWS, Spark, Python).

This timeline illustrates the typical progression from application to final decision. Use this to manage your expectations regarding the duration of the process; while the technical stages can move efficiently, the initial scheduling and feedback loops between rounds may require patience. Ensure you follow up professionally if you experience gaps in communication.

Deep Dive into Evaluation Areas

The technical evaluation at S&P Global is grounded in the practical realities of modern data engineering. Based on candidate reports, the company focuses heavily on the specific tools they use in production. You should not rely solely on theoretical knowledge; be prepared to discuss the nuances of implementation.

Big Data Processing (Spark)

This is a critical evaluation area. You must understand the internals of Apache Spark. It is not enough to know how to write a transformation; you need to understand how Spark executes it.

Be ready to go over:

• Optimization techniques: Understanding the difference between cache() and persist() and when to use each to manage memory and performance.
• Data Structures: The differences between Spark Dataframes and AWS Glue Dynamic Frames (a key topic if the team uses AWS Glue).
• Performance tuning: Handling skew, shuffling, and partitioning strategies.

Python & Scripting

Python is the primary language for data manipulation and orchestration here. The questions often go beyond basic syntax into intermediate and advanced concepts to ensure you can write maintainable, production-grade code.

Be ready to go over:

• Decorators: How to write and apply decorators to modify function behavior (e.g., for logging or timing).
• Generators: Using yield to handle large datasets memory-efficiently.
• Data Structures: Efficient use of lists, dictionaries, and sets for data manipulation.

Database & SQL Mastery

Strong SQL skills are non-negotiable. You will likely face questions on database internals and query optimization, as efficient data retrieval is essential for financial reporting.

Be ready to go over:

• Indexing: How different types of indexes (B-Tree, Bitmap, Clustered) work and how to choose the right one to optimize query performance.
• Query Optimization: Analyzing execution plans and refactoring queries for speed.
• Data Modeling: Concepts around normalization vs. denormalization and schema design.

Cloud Infrastructure (AWS)

Since S&P Global operates heavily in the cloud, you must demonstrate familiarity with the AWS ecosystem. You will be asked to justify your choice of services.

Be ready to go over:

• Service Selection: When to use Redshift vs. Snowflake vs. Athena vs. EMR.
• ETL Orchestration: Experience with AWS Glue, Lambda, and Step Functions.
• Storage: S3 lifecycle policies, partitioning, and storage classes.

ETL Design & DevOps

The interviewers want to know how you build reliable systems. This involves the operational side of data engineering.

Be ready to go over:

• Load Strategies: The logic and implementation differences between Full Loads and Incremental Loads (CDC).
• CI/CD: Managing state files in Jenkins, building pipelines, and automated deployment.
• Scheduling: Handling dependencies and backfills in orchestration tools (Airflow or similar).