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.

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).

The word cloud above highlights the most frequently occurring terms in S&P Global data engineering interviews. Notice the prominence of Spark, AWS, Python, and SQL. Prioritize your revision time around these core technical pillars, ensuring you can speak to them in depth.

Key Responsibilities

As a Data Engineer at S&P Global, your daily work will revolve around the end-to-end lifecycle of data. You will be responsible for designing and implementing scalable data pipelines that ingest data from financial feeds, internal databases, and third-party APIs. A significant portion of your time will be spent writing and optimizing ETL/ELT jobs using Python and Spark to transform raw data into high-quality, analytical datasets.

Collaboration is a major component of this role. You will work closely with data architects to define data models and with data scientists to prepare features for machine learning models. You will also be expected to maintain the health of the data ecosystem. This includes setting up monitoring alerts, troubleshooting pipeline failures in real-time, and ensuring data governance standards are met.

Furthermore, you will likely be involved in modernization initiatives. This could involve migrating legacy on-premise data warehouses to cloud-native solutions on AWS, requiring you to refactor code and redesign workflows for the cloud. You will also manage CI/CD pipelines using tools like Jenkins to ensure smooth and automated deployments of your data applications.

Role Requirements & Qualifications

To be competitive for this position, you need a specific blend of technical skills and professional experience.

Must-Have Skills

• Programming: Advanced proficiency in Python (including decorators, generators, and OOP concepts) and strong SQL skills.
• Big Data Frameworks: Deep hands-on experience with Apache Spark (PySpark/Scala), including performance tuning and memory management.
• Cloud Platforms: Solid experience with AWS services, specifically Glue, EMR, S3, Lambda, and Redshift.
• ETL/ELT: Proven ability to design pipelines handling both full and incremental data loads.

Nice-to-Have Skills

• DevOps Tools: Experience with Jenkins for CI/CD, Terraform for Infrastructure as Code (IaC), and Docker.
• Orchestration: Familiarity with Airflow or AWS Step Functions.
• Domain Knowledge: Previous experience in the financial services, fintech, or credit rating industry.
• Specific AWS Tools: Experience with AWS Glue Dynamic Frames and converting them to/from Spark Dataframes.

Common Interview Questions

The following questions are drawn from recent candidate experiences at S&P Global. They reflect the company's focus on practical implementation and deep technical understanding. While you won't get these exact questions, they represent the types of challenges you will face.

Spark & Big Data

• "What is the difference between cache() and persist() in Spark? When would you use one over the other?"
• "Explain the difference between a Spark Dataframe and an AWS Glue Dynamic Frame. How do you convert between them?"
• "How do you handle data skew in a Spark join operation?"
• "Describe a scenario where you had to optimize a slow-running Spark job. What steps did you take?"

Python & Coding

• "Write a Python decorator that logs the execution time of a function."
• "Explain what a Python generator is and provide a use case for where it is better than a list."
• "How does memory management work in Python?"

Database & SQL

• "How does database indexing work? Explain the difference between a clustered and non-clustered index."
• "Write a SQL query to find the second highest salary in a department."
• "How would you optimize a query that is performing a full table scan on a massive dataset?"

System Design & Architecture

• "Design a pipeline to handle both full loads and incremental loads for a financial dataset. How do you handle updates?"
• "Explain the purpose of a Jenkins state file and how you use Jenkins for CI/CD."
• "Which AWS services would you use to build a data lake for unstructured text data?"

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.

Frequently Asked Questions

Q: How difficult are the technical interviews? Most candidates rate the difficulty as Medium. The questions are not typically designed to trick you but rather to verify that you have the specific skills listed on your resume. Expect a thorough test of your fundamental knowledge in SQL, Python, and Spark.

Q: What is the interview scheduling process like? Candidates have reported that the administrative side can sometimes be slow or disorganized, with potential rescheduling or delays in communication from HR. It is important to stay patient and follow up proactively if you haven't heard back.

Q: Is this a remote role? S&P Global generally operates on a hybrid model, though this can vary by team and location (e.g., Hyderabad, New York, Denver). You should be prepared to discuss your ability to work effectively in a distributed team environment.

Q: How much focus is there on Cloud/AWS? A significant amount. Unlike some roles that are cloud-agnostic, S&P Global interviews often specifically target AWS services. Knowing the specific use cases for AWS Glue, EMR, and Redshift is highly advantageous.

Other General Tips

• Know Your "Why": When discussing AWS services, don't just list what you used. Explain why you chose AWS Glue over EMR for a specific project, or why you used a specific file format (Parquet/Avro). Justification is key.
• Brush Up on Internals: Don't just practice writing code; practice explaining how it works under the hood. For example, knowing how Python handles memory or how Spark creates a DAG (Directed Acyclic Graph) can set you apart.
• Be Patient with Logistics: As noted in candidate feedback, scheduling can sometimes be a pain point. Do not let administrative hiccups affect your mindset or performance during the actual interview.
• Prepare Project Stories: Have a clear, structured story (STAR method) ready about a complex data pipeline you built. Be ready to detail the challenges you faced with data quality or volume.

Summary & Next Steps

Becoming a Data Engineer at S&P Global is an opportunity to work at the intersection of finance and technology. You will tackle complex data challenges that directly influence how the world interprets financial markets. The role demands high technical standards, particularly in Python, Spark, and AWS, but offers the reward of working on high-impact, global-scale systems.

To succeed, focus your preparation on the practical application of Big Data tools. Move beyond the basics of "how to write a query" and master the "how to optimize a system." Review your AWS service knowledge, practice your Python decorators and generators, and ensure your SQL skills are sharp. Approach the process with patience and confidence—your ability to articulate your technical decisions will be your strongest asset.

The salary data provided above gives you a baseline for negotiation. Compensation at S&P Global is generally competitive and includes a mix of base salary and performance bonuses. Be sure to research the specific range for your location and experience level to have an informed discussion during the HR screen.

You have the roadmap. Now, dive into the details and prepare to showcase your engineering expertise. Good luck!