Morgan Stanley's system-design rounds probe low-latency distribution and the trade lifecycle.
In 2026 expect prompts on a market-data distribution system that fans out live prices to thousands of internal consumers, a system tracking a trade through execution, confirmation, and settlement with a full audit trail, and a reconciliation service that compares internal records against a counterparty's. Interviewers reward pub/sub design with back-pressure and slow-consumer handling, clean state-machine modelling of trade lifecycles, and scalable keyed matching for reconciliation. The finance domain makes correctness and auditability first-class concerns, not afterthoughts bolted on at the end.
About Morgan Stanley
Morgan Stanley India (MSCI) is one of the largest global investment banks with a major technology and analytics centre in Mumbai, employing 5,000+ technologists across full-stack, data engineering, and quantitative roles.
Online coding assessment
Technical coding round on DSA
System-design round on a market-data or trade service
Hiring-manager round, then offer
Round 1 (60-90 min)
online coding assessment.
Round 2 (45-60 min)
coding round on data structures and algorithms.
Round 3 (45-60 min)
system-design round on market data, trade lifecycle, or reconciliation.
Round 4 (45 min)
behavioral and hiring-manager round.
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Sign up free: unlock all questionsThe typical Morgan Stanley recruitment process has 4 stages: Online coding assessment → Technical coding round on DSA → System-design round on a market-data or trade service → Hiring-manager round, then offer.
Morgan Stanley typically conducts 4 interview rounds: Round 1 (60-90 min): online coding assessment.; Round 2 (45-60 min): coding round on data structures and algorithms.; Round 3 (45-60 min): system-design round on market data, trade lifecycle, or reconciliation.; Round 4 (45 min): behavioral and hiring-manager round..
HireStepX recommends the Fan-out-and-Lifecycle framework for this type of interview: Design low-latency pub/sub with back-pressure for market data, model the trade lifecycle as an audited state machine, and scale reconciliation with keyed matching
To answer this question well, HireStepX recommends the Fan-out-and-Lifecycle approach: Design low-latency pub/sub with back-pressure for market data, model the trade lifecycle as an audited state machine, and scale reconciliation with keyed matching Ground your answer in a specific real example from your own experience.
To answer this question well, HireStepX recommends the Fan-out-and-Lifecycle approach: Design low-latency pub/sub with back-pressure for market data, model the trade lifecycle as an audited state machine, and scale reconciliation with keyed matching Ground your answer in a specific real example from your own experience.
To answer this question well, HireStepX recommends the Fan-out-and-Lifecycle approach: Design low-latency pub/sub with back-pressure for market data, model the trade lifecycle as an audited state machine, and scale reconciliation with keyed matching Ground your answer in a specific real example from your own experience.
To answer this question well, HireStepX recommends the Fan-out-and-Lifecycle approach: Design low-latency pub/sub with back-pressure for market data, model the trade lifecycle as an audited state machine, and scale reconciliation with keyed matching Ground your answer in a specific real example from your own experience.
To answer this question well, HireStepX recommends the Fan-out-and-Lifecycle approach: Design low-latency pub/sub with back-pressure for market data, model the trade lifecycle as an audited state machine, and scale reconciliation with keyed matching Ground your answer in a specific real example from your own experience.