Factset Data Engineer Interview Guide

Getting ready for a Data Engineer interview at FactSet? The FactSet Data Engineer interview process typically spans a range of technical and scenario-based question topics, evaluating skills in areas like SQL, data pipeline design, ETL systems, data warehousing, and presenting technical solutions to diverse stakeholders. Interview preparation is especially important for this role at FactSet, as candidates are expected to demonstrate not only advanced proficiency in querying and manipulating large datasets, but also the ability to architect scalable data solutions and communicate clearly within collaborative, fast-paced environments.

In preparing for the interview, you should:

• Understand the core skills necessary for Data Engineer positions at FactSet.
• Gain insights into FactSet’s Data Engineer interview structure and process.
• Practice real FactSet Data Engineer interview questions to sharpen your performance.

At Interview Query, we regularly analyze interview experience data shared by candidates. This guide uses that data to provide an overview of the FactSet Data Engineer interview process, along with sample questions and preparation tips tailored to help you succeed.

FactSet is a leading global provider of financial data, analytics, and technology solutions to investment professionals, asset managers, and financial institutions. The company aggregates and analyzes vast datasets to deliver actionable insights that support critical investment decisions. FactSet’s platforms enable clients to monitor global markets, manage portfolios, and conduct in-depth financial research. As a Data Engineer, you will contribute to building and optimizing data pipelines, ensuring the accuracy and reliability of data that underpins FactSet’s comprehensive suite of financial products and services.

As a Data Engineer at Factset, you will design, build, and maintain scalable data pipelines that enable the efficient collection, processing, and integration of financial data from diverse sources. You will collaborate with software engineers, data scientists, and product teams to ensure that data is accurate, timely, and accessible for analytics and client-facing applications. Key responsibilities include developing ETL workflows, optimizing database performance, and implementing data quality controls. This role is essential for supporting Factset’s analytics platforms and helping deliver reliable insights to clients in the financial industry.

2.1 Stage 1: Application & Resume Review

The process begins with a thorough review of your application and resume, focusing on prior experience in data engineering, proficiency in SQL, and a background in designing and maintaining data pipelines. Recruiters look for evidence of strong technical skills, hands-on experience with ETL processes, and knowledge of scalable data architecture. Highlighting relevant projects involving data cleaning, aggregation, and transformation will help your application stand out. Preparation at this stage involves tailoring your resume to showcase measurable impact in previous data engineering roles and aligning your experience with Factset’s focus on real-time analytics and robust data solutions.

2.2 Stage 2: Recruiter Screen

This initial conversation is typically conducted by an HR recruiter and lasts about 30 minutes. The discussion centers on your background, motivation for applying to Factset, and a high-level overview of your technical expertise, especially around SQL and data pipeline projects. While behavioral questions are minimal, you may be asked about your communication style and ability to collaborate with cross-functional teams. To prepare, be ready to succinctly articulate your career journey and why you are interested in Factset’s data-driven environment.

2.3 Stage 3: Technical/Case/Skills Round

The technical assessment is a critical part of the process and may involve a live coding session, a take-home exam, or a panel interview with data engineers and team leads. You can expect multiple SQL challenges, including complex queries, data cleaning scenarios, and real-world data manipulation problems. Additional focus areas include data modeling, algorithmic thinking, and designing scalable ETL pipelines. Interviewers assess not only your technical accuracy but also your ability to explain your approach and optimize for performance. Preparation should center on practicing advanced SQL, reviewing data warehousing concepts, and being ready to discuss previous projects involving large-scale data processing.

2.4 Stage 4: Behavioral Interview

This round is often conducted by hiring managers or senior team members and delves into your problem-solving approach, teamwork, and communication skills. Questions may cover how you handle hurdles in data projects, present complex insights to non-technical stakeholders, and ensure data quality within an ETL setup. Factset values candidates who can translate technical solutions into business impact and collaborate effectively across teams. To prepare, reflect on past experiences where you demonstrated adaptability, clear communication, and a commitment to data quality.

2.5 Stage 5: Final/Onsite Round

The final stage typically involves a series of interviews with directors, senior engineers, and sometimes cross-functional partners. This round may include a technical deep dive, system design scenarios (such as building a scalable data warehouse or real-time streaming solution), and discussions about your approach to data architecture and process optimization. You may also be asked to present a previous project or walk through a case study, emphasizing both technical execution and business relevance. Preparation should focus on end-to-end project narratives, system design best practices, and clear articulation of your technical decisions.

2.6 Stage 6: Offer & Negotiation

Once you successfully complete the previous rounds, the recruiter will reach out with an offer. This stage includes discussions about compensation, benefits, start date, and potential team placement. Factset is open to negotiation for strong candidates, especially those with proven expertise in SQL and large-scale data engineering.

2.7 Average Timeline

The typical Factset Data Engineer interview process spans 3 to 5 weeks from application to offer, with most candidates completing 4 to 5 rounds. Fast-track candidates with strong technical backgrounds may move through the process in as little as 2 to 3 weeks, while the standard pace involves approximately one week between each stage. The technical assessment often requires a prompt turnaround, and panel or onsite interviews are scheduled based on team availability.

Next, let’s dive into the types of interview questions you can expect throughout the Factset Data Engineer process.

3.1. SQL & Data Manipulation

As a Data Engineer at Factset, you’ll be expected to demonstrate strong SQL skills and the ability to handle complex data operations at scale. You’ll encounter questions that test your understanding of data cleaning, transformation, aggregation, and querying efficiency on large datasets.

3.1.1 Write a SQL query to count transactions filtered by several criterias.
Focus on constructing efficient SQL queries using WHERE clauses, aggregate functions, and handling edge cases like missing or inconsistent data. Clearly explain your filtering logic and any optimizations for performance.

3.1.2 Describe a real-world data cleaning and organization project.
Discuss your approach to identifying data quality issues, the tools and techniques used for cleaning, and how you validated the final dataset. Emphasize your attention to detail and reproducibility.

3.1.3 Modifying a billion rows: How would you efficiently update a massive dataset?
Explain strategies for bulk updates, such as batching, indexing, and minimizing downtime. Mention any experience with partitioning, parallel processing, or leveraging cloud-native solutions.

3.1.4 Aggregating and collecting unstructured data.
Describe your process for ingesting, parsing, and structuring unstructured data sources. Highlight your experience with ETL frameworks and any scalable solutions you’ve implemented.

3.1.5 Write a query to compute the average time it takes for each user to respond to the previous system message.
Show how you use window functions and time calculations to align events and compute averages. Address handling missing or out-of-order data.

3.2. Data Pipeline & System Design

Factset values engineers who can design robust, scalable data systems. These questions will assess your ability to architect ETL pipelines, build data warehouses, and manage real-time or batch data flows.

3.2.1 Design a scalable ETL pipeline for ingesting heterogeneous data from Skyscanner's partners.
Outline your approach to modular pipeline design, data validation, and error handling. Discuss scalability and monitoring best practices.

3.2.2 Design a data warehouse for a new online retailer.
Describe your process for schema design, normalization vs. denormalization, and supporting analytics use cases. Consider data sources, refresh rates, and access patterns.

3.2.3 Redesign batch ingestion to real-time streaming for financial transactions.
Explain your choice of streaming technologies, partitioning strategies, and reliability mechanisms. Address latency, fault tolerance, and integration with downstream systems.

3.2.4 Design a solution to store and query raw data from Kafka on a daily basis.
Discuss your approach to schema evolution, efficient storage, and query performance. Mention any experience with columnar formats or distributed file systems.

3.2.5 Design a data pipeline for hourly user analytics.
Describe scheduling, aggregation logic, and handling late-arriving data. Highlight your experience with orchestration tools and monitoring.

3.3. Data Quality & Integrity

Maintaining high data quality is critical at Factset, especially when integrating multiple sources or supporting business-critical analytics. Expect questions on identifying, remediating, and preventing data issues.

3.3.1 How would you approach improving the quality of airline data?
Detail your methods for profiling, cleansing, and validating data. Discuss root cause analysis and implementing automated quality checks.

3.3.2 Ensuring data quality within a complex ETL setup.
Explain strategies for monitoring ETL jobs, handling schema drift, and reconciling discrepancies across sources. Mention any experience with data lineage tools.

3.3.3 Challenges of specific student test score layouts, recommended formatting changes for enhanced analysis, and common issues found in "messy" datasets.
Discuss techniques for transforming and standardizing inconsistent data formats. Emphasize automation and documentation.

3.3.4 You’re tasked with analyzing data from multiple sources, such as payment transactions, user behavior, and fraud detection logs. How would you approach solving a data analytics problem involving these diverse datasets? What steps would you take to clean, combine, and extract meaningful insights that could improve the system's performance?
Describe your approach to data profiling, joining disparate datasets, and resolving conflicts. Highlight your use of metadata and validation frameworks.

3.3.5 Let's say that you're in charge of getting payment data into your internal data warehouse.
Outline your process for data ingestion, error handling, and ensuring data consistency. Address compliance and audit requirements.

3.4. Communication & Presentation

Factset values engineers who can translate technical insights into actionable recommendations for diverse audiences. You’ll need to show how you present findings, collaborate with stakeholders, and make data accessible.

3.4.1 How to present complex data insights with clarity and adaptability tailored to a specific audience.
Discuss tailoring your message, using visuals, and adapting to audience expertise. Share examples of simplifying technical details.

3.4.2 Demystifying data for non-technical users through visualization and clear communication.
Explain your approach to choosing visualizations and crafting clear narratives. Mention any tools or frameworks you use.

3.4.3 Making data-driven insights actionable for those without technical expertise.
Show how you bridge the gap between technical analysis and business decisions. Provide examples of translating jargon into plain language.

3.4.4 User Experience Percentage: How would you measure and communicate user experience improvements?
Describe the metrics you track, how you visualize trends, and how you communicate results to stakeholders.

3.5 Behavioral Questions

3.5.1 Tell me about a time you used data to make a decision that impacted a business outcome.
Describe the context, your analysis, and the recommendation you made. Highlight how your insight led to measurable results.

3.5.2 Describe a challenging data project and how you handled it.
Explain the obstacles, your approach to problem-solving, and the final outcome. Focus on resourcefulness and teamwork.

3.5.3 How do you handle unclear requirements or ambiguity in a data engineering project?
Discuss your strategies for clarifying goals, communicating with stakeholders, and adapting your approach as new information emerges.

3.5.4 Share a story where you used data prototypes or wireframes to align stakeholders with very different visions of the final deliverable.
Describe the tools and methods you used to create prototypes, how you facilitated discussion, and the impact on project alignment.

3.5.5 Describe a time you had to negotiate scope creep when multiple departments kept adding requests to a data project. How did you keep the project on track?
Explain your framework for prioritization, communication, and documenting changes. Highlight your ability to balance competing needs.

3.5.6 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.
Share your approach to building consensus, presenting evidence, and following up on outcomes.

3.5.7 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again.
Describe the automation tools or scripts you developed, how you implemented them, and the impact on team efficiency.

3.5.8 Describe a time you delivered critical insights even though a significant portion of the dataset had nulls. What analytical trade-offs did you make?
Explain your approach to handling missing data, communicating uncertainty, and ensuring actionable insights.

3.5.9 How do you prioritize multiple deadlines? Additionally, how do you stay organized when you have multiple deadlines?
Discuss your system for task management, communication, and adapting to shifting priorities.

3.5.10 Tell me about a time when you exceeded expectations during a project.
Describe how you identified opportunities to go above and beyond, the actions you took, and the results achieved.

4.1 Company-specific tips:

Immerse yourself in FactSet’s core business by studying how financial data is aggregated, processed, and delivered to clients. Understand the company’s commitment to data accuracy and reliability, as these are central to their reputation in the financial analytics space. Research FactSet’s suite of products and platforms, paying attention to how data engineering enables real-time analytics, portfolio management, and financial research. Be ready to discuss how your work as a Data Engineer supports FactSet’s mission to provide actionable insights for investment professionals and financial institutions.

Demonstrate your familiarity with the challenges of handling large-scale financial datasets. FactSet works with diverse sources, requiring robust data integration and quality control. Show that you appreciate the importance of data lineage, compliance, and auditability in a regulated industry. Reference any experience you have with financial data, market feeds, or regulatory requirements, and be prepared to articulate how you would ensure data integrity and reliability in FactSet’s environment.

Highlight your ability to collaborate cross-functionally. FactSet’s data engineers work closely with software engineers, data scientists, and product teams. Emphasize your communication skills and your experience translating technical solutions into business impact. Prepare examples that show how you’ve worked with stakeholders to deliver data-driven solutions that support product development or client needs.

4.2 Role-specific tips:

4.2.1 Master advanced SQL techniques for large-scale data manipulation and optimization.
Practice writing complex SQL queries that involve joins, aggregations, window functions, and filtering on massive datasets. Be prepared to discuss query optimization strategies, such as indexing, partitioning, and minimizing resource usage. Show your ability to handle real-world scenarios like updating billions of rows efficiently or cleaning and transforming messy data.

4.2.2 Be ready to design robust, scalable ETL pipelines and data warehouses.
FactSet will test your ability to architect end-to-end data solutions. Prepare to outline your approach to designing ETL workflows for heterogeneous data sources, including modular pipeline construction, error handling, and monitoring. Discuss schema design, normalization versus denormalization, and how you would support analytics requirements in a data warehouse setup.

4.2.3 Demonstrate expertise in real-time and batch data processing.
Expect questions on transitioning from batch ingestion to real-time streaming architectures, especially for financial transactions. Be ready to explain your choice of technologies, strategies for handling latency and fault tolerance, and how you ensure reliability and scalability in high-throughput environments.

4.2.4 Show your commitment to data quality and integrity.
FactSet places a premium on clean, reliable data. Prepare to discuss your methods for profiling, cleansing, and validating datasets from multiple sources. Talk about how you implement automated quality checks, monitor ETL jobs for schema drift, and reconcile discrepancies across systems. Share examples of automating data-quality checks to prevent recurring issues.

4.2.5 Illustrate your ability to communicate complex technical insights effectively.
FactSet values engineers who can make data accessible to both technical and non-technical audiences. Practice explaining your technical decisions, presenting data insights with clarity, and adapting your message for different stakeholders. Use examples of how you’ve used visualizations or simplified technical jargon to drive business decisions.

4.2.6 Prepare to discuss behavioral scenarios involving teamwork, ambiguity, and stakeholder management.
Reflect on past experiences where you navigated unclear requirements, negotiated scope changes, or influenced stakeholders without formal authority. Be ready with stories that showcase your adaptability, organizational skills, and ability to deliver results under pressure.

4.2.7 Highlight your experience with automation and process improvement in data engineering.
FactSet appreciates candidates who proactively improve data workflows. Share examples of automating recurrent data-quality checks, streamlining ETL processes, or implementing monitoring solutions that enhanced team efficiency and data reliability.

4.2.8 Be ready to analyze and synthesize insights from diverse, messy datasets.
Discuss your approach to integrating multiple data sources, handling missing or inconsistent data, and extracting actionable insights even when faced with imperfect information. Emphasize your analytical rigor and ability to communicate trade-offs when data quality is less than ideal.

5.1 How hard is the Factset Data Engineer interview?
The Factset Data Engineer interview is challenging, with a strong emphasis on advanced SQL, scalable data pipeline design, and real-world problem solving. Candidates are expected to demonstrate deep technical expertise in manipulating large datasets, architecting robust ETL workflows, and ensuring data quality. You’ll also be evaluated on your ability to communicate complex solutions clearly and collaborate within cross-functional teams. Preparation and confidence in both technical and behavioral scenarios are key to success.

5.2 How many interview rounds does Factset have for Data Engineer?
Factset typically conducts 4 to 5 interview rounds for Data Engineer candidates. These include the initial recruiter screen, a technical or case-based assessment, a behavioral interview, and a final onsite or virtual round with senior engineers and directors. Each round targets specific skill sets, from hands-on coding to system design and stakeholder communication.

5.3 Does Factset ask for take-home assignments for Data Engineer?
Yes, Factset may include a take-home technical assignment as part of the Data Engineer interview process. This assignment often involves designing or implementing a data pipeline, solving complex SQL challenges, or addressing a real-world data engineering scenario. The goal is to assess your practical skills, problem-solving approach, and ability to deliver high-quality solutions independently.

5.4 What skills are required for the Factset Data Engineer?
Key skills for Factset Data Engineers include advanced SQL, ETL pipeline design, data warehousing, data modeling, and experience with both batch and real-time data processing. Proficiency in handling large, messy datasets, implementing data quality controls, and optimizing database performance is essential. Strong communication skills and the ability to collaborate across technical and non-technical teams are highly valued.

5.5 How long does the Factset Data Engineer hiring process take?
The Factset Data Engineer hiring process typically spans 3 to 5 weeks from application to offer. Fast-track candidates may complete the process in as little as 2 to 3 weeks, while most experience approximately one week between each interview stage. Timelines can vary based on team availability and candidate scheduling.

5.6 What types of questions are asked in the Factset Data Engineer interview?
Expect a mix of technical and behavioral questions, including advanced SQL coding challenges, data cleaning and transformation scenarios, ETL pipeline design, data warehouse architecture, and system design for real-time streaming. You’ll also face behavioral questions about teamwork, stakeholder management, and communication, as well as case studies focused on data quality and business impact.

5.7 Does Factset give feedback after the Data Engineer interview?
Factset typically provides feedback through recruiters, especially for candidates who progress to later stages. While detailed technical feedback may be limited, you will often receive high-level insights on your performance and areas for improvement.

5.8 What is the acceptance rate for Factset Data Engineer applicants?
The acceptance rate for Factset Data Engineer roles is competitive, estimated at around 3-5% for qualified applicants. Factset seeks candidates with strong technical backgrounds, proven experience in data engineering, and the ability to thrive in a fast-paced, collaborative environment.

5.9 Does Factset hire remote Data Engineer positions?
Yes, Factset offers remote Data Engineer positions, with some roles requiring periodic office visits for team collaboration or onboarding. The company supports flexible work arrangements, especially for candidates with strong technical and communication skills who can contribute effectively from a remote setting.

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