Astrotalk SDE-1 Interview Experience | 4 Rounds
Summary
I interviewed for an SDE-1 role at Astrotalk across four rounds, covering Java/Spring Boot fundamentals, DSA, system design, and backend concepts, and I cleared all rounds but later declined the offer.
Full Experience
I recently got the opportunity to interview at Astrotalk.
YOE: 0 (1 year in internships) 2026 Graduate, IIT
Round 1 (Virtual)
Started the discussion around my resume with a few follow-ups on whatever work I had done.
After that, the discussion moved to Java/Spring Boot fundamentals, as I have mostly worked with them. Things like:
- Autowired
- AOP
- Dependency Injection
- Circular Dependency
Then the discussion moved to real-world scenarios like:
- Designing an API to handle duplicate button clicks
- Rate limiting notifications
DSA
-
Edit Distance Only brute force was required.
-
Lowest Common Ancestor of a Binary Tree
In the end, there were some puzzles from Brainstellar.
Verdict: Cleared R1 comfortably
Round 2 (Virtual)
The interview was mostly around backend, system design, databases, Kafka, caching and some experience-based questions.
It started with resume discussion. I had worked around SQS and Kafka previously, so there were many follow-ups regarding both SQS and Kafka, like:
- Why did you choose Kafka?
- How would you make a Kafka consumer idempotent?
- Kafka partitions, keys and the relationship between partitions and consumers
- How would you optimize a slow API?
- Database query optimization and indexes
- Composite index for
WHERE country = ? AND state = ? ORDER BY creation_time DESC - How would you parallelize independent operations in an API?
- Design an astrologer-listing service with Redis caching
- Cache stampede and how to handle it
- What happens if Redis goes down? How would you design a fallback?
- Circuit breaker and its use
- Choosing RDBMS vs NoSQL for a chat service
- SQS vs Kafka: when would you choose one over the other?
- Biggest/difficult problem you worked on
Ended with a few questions around team structure, day-to-day work, ownership and engineering challenges.
Verdict: Cleared R2 comfortably
Round 3 (Noida Office)
This round started similarly to the previous round. The first half an hour was mostly around my work and follow-ups on that.
After that, we had discussions based on real-world scenarios.
One of the main questions was to design a system for Astrologer Suggestions in the app. Basically, which astrologers should be visible to a user.
The design had to consider scenarios like:
- Paid astrologers
- Free astrologers
- Region-specific astrologers
- Language preferences
- Other relevant factors
There were some follow-ups on the design and one more specific scenario that I don't remember exactly.
Round 4 (Same Day, CTO)
This round was short, around 20-25 minutes.
Started with discussion around my previous work and then moved to some DSA and CS fundamentals.
Questions/topics included:
- Merge Sort
- Intuitive meaning of
log(n) - Explaining why the complexity of Merge Sort is
O(n log(n)) - Cache
- LRU Cache algorithm
- Basic OS concepts
Verdict: Selected
Final Outcome
I rejected the offer as I received another offer with better compensation.
Astrotalk Offer: 16 LPA Base + 2 LPA Retention Bonus
Overall, the interviews were quite good and focused heavily on practical backend knowledge, system design and understanding of things I had actually worked on.
For someone with 0 YOE but good internship experience, I found the interviews pretty fair. Most of the questions were around fundamentals and being able to explain the reasoning behind your decisions rather than just knowing definitions.
Interview Questions (8)
Edit Distance
Given two strings, compute the minimum number of operations (insert, delete, replace) required to convert one string into the other.
Lowest Common Ancestor of a Binary Tree
Given a binary tree and two nodes, find their lowest common ancestor.
Merge Sort Complexity Explanation
Explain why the time complexity of Merge Sort is O(n log n) and discuss its intuitive meaning.
LRU Cache Algorithm
Describe the LRU (Least Recently Used) cache algorithm, its operations and how it manages eviction.
Design API to Handle Duplicate Button Clicks
Design an API endpoint that safely handles cases where a user may click a button multiple times, ensuring idempotency and preventing duplicate processing.
Rate Limiting Notifications
Design a mechanism to limit the rate at which notifications are sent to users, preventing abuse while allowing legitimate traffic.
Design Astrologer‑Suggestion Service
Design a system that suggests astrologers to a user based on factors such as paid vs free status, region, language preferences, and other relevant criteria.
Design Astrologer‑Listing Service with Redis Caching
Design a service that lists astrologers using Redis as a cache, handling cache stampede, Redis failures, and providing fallback strategies.