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.
- Products
- Institutional equity trading platforms · Fixed income analytics · Wealth management systems (Morgan Stanley Smith Barney) · Risk and compliance systems · E*TRADE (retail brokerage, acquired 2020)
- Competitors
- Goldman Sachs · JPMorgan Chase · Barclays Capital · Bank of America Merrill Lynch
- Scale
- ~6,000 employees in India (Mumbai primarily). NYSE: MS. Global revenue ~$55B. Mumbai tech centre is a Tier-1 engineering hub, not just support.
Morgan Stanley recruitment process
01
02
Technical coding round on DSA
03
System-design round on a market-data or trade service
04
Hiring-manager round, then offer
What to expect in each round
01
Round 1 (60-90 min)
online coding assessment.
02
Round 2 (45-60 min)
coding round on data structures and algorithms.
03
Round 3 (45-60 min)
system-design round on market data, trade lifecycle, or reconciliation.
04
Round 4 (45 min)
behavioral and hiring-manager round.
System Design questions Morgan Stanley asked
Sourced from 2+ candidate post-mortems. Hit Practice to answer any one with AI voice feedback.
- 01
Hard
Design a system to predict same-day delivery feasibility for a new pincode in tier-3 India.
- 02
Hard
We're seeing UPI failure rates spike at 9pm on weekends. How would you debug, and what's your hypothesis?
- 03
Hard
Walk me through how you'd build idempotency into a payment retry system. What happens if the network drops mid-callback?
- 04
Hard
Design Swiggy Genie's matching algorithm. How does it differ from food-order matching?
- 05
Hard
Walk me through how you'd architect transaction-level fraud detection for 100M daily UPI transactions.
- 06
Medium
How would you build a recommendation engine that suggests bills the user is about to forget?
- 07
Medium
Design a cashback-allocation system that prevents abuse without alienating genuine users.
- 08
Hard
Design a system to detect duplicate documents at web scale. What's your approximation strategy?
- 09
Hard
Design Amazon Prime's recommendation engine. How would you handle the cold-start for a new Prime member?
- 10
Medium
Design a URL shortener like bit.ly. Walk me through database choice and scaling.
- 11
Hard
Design Instagram's news feed ranking. How do you handle a celebrity who posts 50 times an hour?
- 12
Hard
Design Amazon's order tracking notification system. Optimise for cost at 100M orders/day.