S&P Global Data Engineer Interview Questions (2026)
Prepare for your technical rounds at S&P Global with 10+ reported questions across SQL, Apache Spark, Python, and System Design. Compare typical candidate answers against FAANG 9/10 benchmarks, and test your own solutions with instant AI feedback.
Interview Topic Weightage at S&P Global
Based on verified interview loopsUnlocked FAANG-Calibrated Answer Rubric (Top 3 Tested Questions)
- •Structured delivery following Problem Formulation -> Execution Plan -> Failure Recovery
- •Concrete parameters: partition keys, join strategies, memory limits, and I/O costs
- •Explicit trade-off evaluation (e.g. storage cost vs query latency, exactly-once overhead)
Complete S&P Global Question Vault (7 Questions)
Showing 7 of 7What is the size of the teams I've worked with and how we handled sprints during the project?
Situation: Team and process. Task: Describe scale and agility. Action: Teams: 4-15 engineers. Sprints: 2-week cycles, planning, standups, retros. Story points, velocity, backlog grooming. Cross-team sync for large projects. Balanced planned work with ops capacity. Tech debt time. Adapted ceremony to need....
Why are you considering leaving your current company?
Situation: Leaving rationale. Task: Positive framing. Action: Seeking growth, new challenges, better fit. Grateful for experience. Specific reason (limited growth, scale, domain, culture). Leaving on good terms. Focus on what I'm moving toward....
Explain the differences between Redshift and Snowflake, and how I've used them in previous projects.
**Redshift**: AWS-native, tightly coupled storage/compute, cluster-based; Spectrum for S3 queries; Concurrency Scaling for read bursts. **Snowflake**: Cloud-agnostic, decoupled storage/compute, per-second billing; zero-copy clone, Time Travel, Data Sharing. **Why Redshift**: Lower TCO for 24/7 steady workloads; deep AWS integration (Glue, Lambda); reserved instances cut cost 40%. **Why Snowflake**: Variable/spiky workloads; multi-cloud; DevProd parity via clones; sharing for data marketplace....
Explain the scalability, performance, and cost-efficiency of both Redshift and Snowflake in different use cases.
**Scalability**: Redshift—vertical (add nodes) + Concurrency Scaling for query bursts; Snowflake—auto-scale warehouses, storage scales independently. **Performance**: Both columnar; Snowflake caches query results (persistent); Redshift relies on local SSD. Snowflake multi-cluster warehouses handle concurrency; Redshift WLM queues....
Write a query to find the 5th highest salary in an employee table and calculate the number of employees whose salary is greater than that of their manager.
(1) 5th highest: SELECT salary FROM (SELECT DISTINCT salary, DENSE_RANK() OVER (ORDER BY salary DESC) r FROM employees) WHERE r = 5. (2) Employees > manager: SELECT COUNT(*) FROM employees e JOIN employees m ON e.manager_id = m.employee_id WHERE e.salary > m.salary. **Why**: Combine in CTEs if single query needed....
Explain how I handle performance optimizations, scheduling tasks, and monitoring DAGs in Airflow.
**Situation**: Faced competing demands—multiple pipelines, stakeholders, deadlines. **Task**: Deliver impact while maintaining quality and preventing burnout. **Action**: (1) Prioritized by business impact and SLA risk. (2) Used ROI (value/time); WIP limits; timeboxing. (3) Communicated trade-offs—'Adding X pushes Y by N days.' (4) Maintained backlog with tech-debt capacity. **Result**: Shipped on time; zero incidents; stakeholder alignment on deferrals....
Provide specific examples of challenges faced with PySpark and SQL and solutions implemented.
**Situation**: Production join job on 10B-row fact table with 100M-row dimension timed out; one task ran 2h vs 5min for others. **Task**: Identify root cause and fix without schema changes. **Action**: (1) Profiled via Spark UI—identified skew on region key (80% data in 3 partitions); (2) Implemented salting: replicated dimension with salt range, joined on (key, salt), aggregated; (3) Enabled AQE skew join as fallback. **Result**: P99 dropped 70%; job met SLA; pattern documented for team....
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Frequently Asked Questions About S&P Global Data Engineering Interviews
What topics are asked most frequently in S&P Global data engineering interviews?▼
Technical interviews at S&P Global heavily emphasize Spark/Big Data and SQL, accounting for the majority of technical questions in our repository of 10+ verified interview questions. Candidates should expect hands-on rounds testing query optimization, data skew mitigation, and end-to-end pipeline architecture.
How deep do S&P Global interviewers go on Apache Spark, SQL, and system design?▼
Interviewers at S&P Global probe far beyond syntax. They expect candidates to discuss Spark DAG execution plans, shuffle partitions, join selection strategies (broadcast hash vs shuffle sort-merge), and handling un-even data distributions. In system design, emphasis is placed on fault tolerance, backfilling, and SLA preservation under high data volume.
What is the typical technical interview loop format for Data Engineers at S&P Global?▼
The S&P Global interview process typically consists of: 1) Initial Recruiter Screen (30 mins), 2) Technical Phone Screen focusing on live SQL and data manipulation (45-60 mins), followed by a 4-5 round Virtual Onsite covering SQL & Coding, Distributed Pipeline Architecture, Data Modeling, and a Behavioral/Bar-Raiser session.
How can I practice S&P Global data engineer interview questions with AI feedback?▼
You can practice directly on DataEngPrep.tech. Select any question from the S&P Global library, submit your answer, and receive an instant FAANG-calibrated evaluation scored across accuracy, technical depth, communication, and completeness, complete with an interview-ready model answer rewrite.