Factset interviews are generally considered medium to hard difficulty, focusing heavily on clean, efficient code and problem-solving clarity. The process is similar to Amazon in its behavioral emphasis (using Leadership Principles), but the technical rounds often involve more domain-agnostic data structures and algorithms without the extreme "trick" questions sometimes seen at Google or Meta. Expect a strong focus on writing production-quality code with attention to edge cases.
Aim for 10-12 weeks of dedicated preparation (2-3 hours daily). Your routine should split time between 1.5 hours of DSA (solving 1-2 LeetCode medium/hard problems, focusing on variability), 0.5 hours reviewing Factset's specific tech stack (C++, Java, Python) and financial data concepts, and 0.5 hours practicing behavioral stories using the STAR method aligned to their 10 Leadership Principles. In the final two weeks, take 3-4 timed, full-length mock interviews.
Prioritize core areas: Arrays, Strings, Linked Lists, Trees (Binary, BST), Graphs (BFS/DFS), Hash Maps, Heaps, and Recursion/Backtracking. Factset frequently tests problem-solving on these fundamentals, especially questions involving data manipulation, optimization, and designing APIs for data processing. Ensure you can explain time/space complexity for every solution and write bug-free code on a whiteboard or in a shared doc.
The biggest mistake is providing vague stories without concrete metrics or clear links to Factset's Leadership Principles. Candidates often focus only on the "what" and not the "how" or "why." Prepare 8-10 detailed stories using the STAR format, each explicitly tied to principles like "Customer Obsession" or "Earn Trust," and practice quantifying your impact (e.g., "improved efficiency by 30%" or "reduced errors by X").
For higher-level roles, standing out requires demonstrating system design thinking and ownership. In your technical rounds, proactively discuss trade-offs, scalability, and API design even for coding questions. In behavioral rounds, highlight experiences mentoring junior engineers, leading project decomposition, and making decisions under ambiguity. Showing a genuine interest in financial data and markets will also differentiate you significantly.
The timeline is usually 4-6 weeks. After applying, you may hear back within 1-2 weeks for an initial screen. The full loop (4-5 interviews) is often scheduled within 2-3 weeks of the screen, and a hiring decision is made within 1-2 weeks after that. If you haven't heard anything 2 weeks after your final interview, a polite email to your recruiter is appropriate to reiterate your interest.
SDE-1 focuses on strong DSA implementation and learning. SDE-2 expects deeper problem analysis, some system design (e.g., design a key-value store), and behavioral stories showing project leadership. SDE-3 requires advanced, multi-component system design (design a financial data pipeline), evaluation of technology choices, and behavioral examples demonstrating architectural influence, mentorship, and cross-team collaboration. The bar for clarity, scalability, and business impact reasoning rises significantly with each level.
Combine standard resources with Factset-specific research. Use LeetCode (filter by company tag for recent patterns) and "Designing Data-Intensive Applications" for system design. Crucially, study Factset's website, annual reports, and product pages (like FactSet Workstation) to understand their business model. On Glassdoor, look for recent interview experiences mentioning "data processing," "API design," and questions about optimizing queries on large datasets. Practice articulating how your skills solve real financial data challenges.
