Indian product companies vs FAANG: what changes
In one line
The core skills are the same, but Indian product companies lean more on low-level design, machine coding and real production experience at Indian scale, while FAANG loops are more standardised and score generic distributed-systems reasoning.
What it is
Both Indian product companies (Flipkart, Swiggy, Zomato, Razorpay, PhonePe, CRED, Zerodha, Meesho and others) and FAANG offices in India test system design for mid and senior roles. The framework of requirements, estimates, API, data model, high-level design and deep dives works everywhere.
What changes is the format and the emphasis. Many Indian product companies run a machine coding round (build a small working program, such as a parking lot or a splitwise clone, in 60 to 120 minutes) and a low-level design (LLD) round on classes, interfaces and design patterns, alongside a high-level design (HLD) round. FAANG loops usually have one or two HLD rounds with standard rubrics and a separate committee or debrief that decides level.
The problems also differ in flavour. Indian companies often ask designs close to their own business and to Indian conditions: flash sales, UPI payments, delivery partner assignment, live cricket scores, regional languages, and cost per user at low average revenue. FAANG prompts tend to be more generic, such as a key-value store, a news feed or a rate limiter, and probe distributed systems fundamentals.
Why interviewers ask
Candidates who prepare only for FAANG-style HLD often fail the machine coding or LLD round at Indian companies, and candidates who prepare only for LLD are caught out by FAANG's depth on consistency and scale. Knowing the differences lets you split preparation time sensibly when interviewing at both.
How it works
- 1Read the job description and recruiter notes to learn the rounds. Ask the recruiter directly: 'Is there a machine coding or LLD round, and how many design rounds are there?'
- 2For Indian product companies, practise machine coding: clean, extensible, working code in your language within a time box, with in-memory storage, clear interfaces and a few tests. Evaluators look at modularity, naming and handling of edge cases more than at fancy patterns.
- 3Practise LLD separately: class diagrams, responsibilities, SOLID principles, and patterns such as strategy, factory and observer applied to problems like a ride matching engine or a rate limiter class.
- 4For HLD at Indian companies, expect questions tied to the domain: 'design our order tracking' at a delivery company, 'design a payment status system' at a fintech. Bring Indian constraints: peak events, flaky mobile networks, cost per order, RBI rules for payment data.
- 5Expect deep questions on your own past projects: the scale you ran, an incident you handled, trade-offs you made. Hiring manager rounds often weigh ownership heavily.
- 6For FAANG, practise generic HLD problems with strict structure and precise vocabulary, and prepare behavioural stories for the company's framework, such as Amazon's Leadership Principles.
- 7At both, numbers and trade-offs win. The difference is mostly which extra rounds and context you must prepare for, not a different way of thinking.
Worked example · One candidate, two loops for SDE-2
- Indian e-commerce company: round 1 is machine coding, 90 minutes, 'build an inventory and cart system with add, remove, checkout and discounts'. The candidate writes modular code with a DiscountStrategy interface and passes edge cases like empty carts.
- Round 2 is LLD on the same domain, extending it for flash-sale limits per user.
- Round 3 is HLD: 'design the sale-day checkout for 10x normal traffic'. The interviewer pushes on inventory reservation, payment gateway timeouts and what to switch off (recommendations, reviews) to protect checkout.
- Round 4 is the hiring manager: deep questions on a service the candidate owned and an incident they handled.
- FAANG loop: two coding rounds on algorithms, one HLD round ('design a distributed rate limiter'), one behavioural round. The HLD is scored on a standard rubric and reviewed with the other rounds before a level is decided.
- The candidate's HLD preparation is reused in both loops; the extra work for the Indian loop was machine coding and LLD practice.
Picture it
It is like playing cricket in different formats. Test cricket and IPL both need batting, bowling and fielding, but the format changes what you practise: patience for one, quick scoring and specialist roles for the other. A good player prepares for the format of the match they are playing.
Key patterns
Shared core, specific extras
When: Interviewing at both kinds of companies
How: Spend most of the time on HLD fundamentals that serve both, then add machine coding and LLD for Indian companies, and behavioural framework practice for FAANG.
Bring local context
When: HLD at an Indian company
How: Add one or two India-specific constraints where they fit: peak events, cost per order, poor networks in Tier-2 and Tier-3 cities, regional language search, data localisation. Do not force them.
Speak about your own production systems
When: Hiring manager and project deep-dive rounds
How: Prepare numbers (QPS, data size, latency), one incident with root cause and fix, and one trade-off you would make differently now.
Real-world use
Indian scale events
Indian companies design for very sharp peaks: festive e-commerce sales, IRCTC Tatkal windows at 10 AM and 11 AM, and cricket streams where Hotstar reported about 25 million concurrent viewers in 2019. Expect prompts that stress such peaks.
RBI data localisation
Since a 2018 RBI directive, payment system data must be stored only in India. Fintech design answers that mention where data lives and how it is replicated within Indian regions show domain awareness.
UPI and payments
UPI handles billions of transactions a month across many banks and apps. Fintech interviews often probe timeouts with banks, pending states, reconciliation and idempotent retries, because these are daily problems in UPI flows.
FAANG levelling process
Google uses hiring committees, Amazon uses a Bar Raiser and Leadership Principles, and Meta uses standard rubrics and a debrief. These processes make FAANG loops more uniform across interviewers than at many smaller companies.
In a project
You have 6 weeks to prepare for SDE-2 loops at a fintech in Bengaluru and at a FAANG company's India office.
- Confirm the round structure with both recruiters in week 1.
- Weeks 1 to 4: three HLD problems a week, shared by both loops, with timed practice and written estimates.
- Weeks 1 to 3: two machine coding problems a week in a 90-minute time box, reviewed for modularity and tests.
- Weeks 3 to 5: LLD practice on payment-flavoured problems (wallet, ledger, refund state machine) plus HLD questions on payment status, webhooks and reconciliation.
- Weeks 4 to 6: behavioural stories for FAANG and deep notes on your own past systems for the hiring manager round.
- Final week: mixed mocks in both formats.
Impact: You walk into both loops prepared for every round type, reuse most of the HLD work, and avoid the common failure of a strong HLD but an unfinished machine coding round.
Trade-offs
Use it when
- Planning preparation for a mix of Indian product companies and FAANG
- Choosing which domain examples to use in an HLD round
- Preparing for a company whose round format you do not know yet
Avoid it when
- Assuming every Indian company has the same rounds; formats vary and change, so confirm with the recruiter
- Forcing Indian examples into a generic FAANG prompt where they add nothing
Common mistakes
- Skipping machine coding practice and running out of time with non-working code.
- Over-using design patterns in machine coding instead of writing simple, working, extensible code.
- Preparing only generic HLD problems for a domain-heavy interview at a fintech or delivery company.
- Ignoring cost; Indian companies often care a lot about cost per order or per user.
- Not being able to explain the numbers and incidents of your own past projects.
- Assuming FAANG India loops are easier or different in bar from FAANG elsewhere; the rubrics are generally the same.
Interview questions
Quick revision
- Same core: requirements, numbers, API, data, HLD, deep dives.
- Indian product companies often add machine coding and LLD rounds.
- FAANG: standard rubrics, committee or debrief, behavioural framework.
- Ask the recruiter for the exact rounds.
- Machine coding: working, modular, extensible, tested, on time.
- Indian HLD: peaks, cost per order, mobile networks, languages, RBI data rules.
- Know your own systems: numbers, incidents, trade-offs.
- Use local context where it fits; never force it.
Check yourself
1. Which round is common at Indian product companies but rare in FAANG loops?
2. Which constraint is most relevant when designing payment data storage for an Indian fintech?
3. What is the best way to prepare when interviewing at both a FAANG office and an Indian fintech?
4. In a machine coding round, what do evaluators value most?
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