Groww Interview Questions India 2026: SDE, Data Engineer & Product Manager Roles

For SDE roles (2–6 years experience):

Round 1: Online Assessment

• 2 DSA problems (Medium to Hard), 90 minutes on HackerRank
• Data structures: arrays, strings, trees, graphs, DP
• Most commonly Medium-level; at least one Hard problem in every set

Round 2: Technical Interview 1 (DSA deep-dive)

• 1–2 coding problems solved live on a shared editor
• Expect follow-ups: 'What is the time complexity? Can you do it in O(n)? What if the input had duplicates?'
• You need to talk through your approach before coding: silent coding is penalised

Round 3: Technical Interview 2 (System Design)

• Design a real-time stock price update system
• Design Groww's portfolio view with millions of concurrent users
• Design a notification system for price alerts
• Topics: WebSocket vs polling, Redis pub/sub, database sharding, caching strategies

Round 4: Technical + Behavioural (with team lead or EM)

• Mix of technical and situational questions
• 'Describe a time you disagreed with a technical decision and how you handled it'
• 'What's the biggest production incident you've been part of?'
• Code quality discussion: how do you approach testing? What's your deployment philosophy?

Round 5: Bar Raiser / Culture Fit (Director or VP)

• Not a technical round
• Groww values: user-centricity, ownership, first-principles thinking
• Questions: 'Why do you want to work at a fintech vs a FAANG?', 'What have you built that you're most proud of?', 'How do you think about product quality?'

These are reported by candidates in 2024–26 interview rounds:

Arrays and Strings:

• Find the maximum sum subarray (Kadane's algorithm)
• Longest substring without repeating characters
• Two Sum and its variants (three sum, closest to target)
• Merge intervals: often asked with a stock market framing ('merge overlapping trading sessions')
• Rotate a matrix 90 degrees in-place

Linked Lists:

• Reverse a linked list in groups of K
• Detect and find the start of a cycle
• Merge K sorted linked lists

Trees:

• Level order traversal with zigzag pattern
• Lowest common ancestor of two nodes
• Diameter of a binary tree
• Serialize and deserialize a binary tree

Dynamic Programming (common at Groww):

• Coin change problem (minimum coins)
• Longest increasing subsequence
• Edit distance between two strings
• 0/1 knapsack variants

Graphs:

• Number of islands (BFS/DFS)
• Shortest path (Dijkstra in weighted graphs)
• Detect cycle in a directed graph
• Topological sort (dependency resolution: 'given a list of stock processing dependencies, find the order')

Groww-specific framing: Many problems are framed in fintech context: 'process stock orders', 'calculate portfolio return', 'sort watchlist by volume': the algorithm is standard but the problem statement is domain-specific. Don't let the framing confuse you; identify the underlying algorithm.

System design at Groww focuses on fintech-relevant scale problems. Key themes: real-time data, high read throughput, consistency requirements.

Question 1: Design a real-time stock price feed for Groww Expected discussion points:

• Data source: NSE/BSE WebSocket feed vs REST polling
• Fan-out: How do you push updates to 12M users without overwhelming your servers?
• Architecture: Redis pub/sub for in-memory price cache, WebSocket connections for premium users, long-polling for mobile app battery optimisation
• Consistency: stock price updates are eventually consistent; exact real-time is aspirational, 1–2 second lag is acceptable
• Load estimation: 2,000 active stocks × 1 update/second during market hours = 2,000 messages/second inbound

Question 2: Design Groww's portfolio value calculator Expected discussion points:

• Data model: user holdings (stock + quantity + avg price), live prices (Redis), portfolio value = sum(quantity × live_price)
• How do you handle market hours vs after-hours? Pre-market vs market open price?
• Read vs write ratio: reads dominate (users check portfolio frequently), writes happen at order execution
• Caching: per-user portfolio cache invalidated on order events

Question 3: Design a price alert notification system Expected discussion points:

• Alert types: above/below threshold, % change
• Alert storage: user ID → list of alerts; stock → list of users watching it
• Trigger evaluation: for each stock update, check if any user's alert is triggered (set intersection)
• Delivery: WebSocket for in-app, FCM push for mobile, email for inactive users
• Deduplication: don't send the same alert twice if price bounces around the threshold

Groww has a strong data team: products like SIP performance analysis, portfolio benchmarking, and 'Nudge' recommendations are built on top of user event data.

SQL Questions (always asked): ```sql -- Q1: Find users who made their first SIP in the last 30 days SELECT DISTINCT userid FROM transactions WHERE transactiontype = 'SIP' AND transactiondate >= CURRENTDATE - INTERVAL '30 days' AND userid NOT IN ( SELECT userid FROM transactions WHERE transactiontype = 'SIP' AND transactiondate < CURRENT_DATE - INTERVAL '30 days' );

-- Q2: Find the 3rd most traded stock by volume in the last week SELECT stocksymbol, SUM(quantity) as totalvolume FROM trades WHERE tradedate >= CURRENTDATE - INTERVAL '7 days' GROUP BY stocksymbol ORDER BY totalvolume DESC LIMIT 1 OFFSET 2;

-- Q3: Month-over-month user retention WITH monthlyactive AS ( SELECT userid, DATETRUNC('month', eventdate) as month FROM events GROUP BY 1, 2 ) SELECT m1.month, COUNT(DISTINCT m2.userid) * 100.0 / COUNT(DISTINCT m1.userid) as retentionrate FROM monthlyactive m1 LEFT JOIN monthlyactive m2 ON m1.userid = m2.user_id AND m1.month = m2.month - INTERVAL '1 month' GROUP BY m1.month; ```

Python / Data questions:

• Clean a transactions dataset with missing prices and duplicate entries
• Calculate CAGR of a portfolio given transaction history
• Aggregate daily returns into a rolling 7-day average return

Product Sense (BI/Analyst):

• 'Groww's SIP conversion rate dropped 12% this week. How do you diagnose it?'
• 'How would you measure the success of a new educational content feature?'

Groww salaries are Tier-1 Indian product company level, comparable to Razorpay and Swiggy.

SDE-1 (fresher / 0–2 years): ₹15–28 LPA total compensation SDE-2 (2–5 years): ₹28–55 LPA SDE-3 / Senior (5–8 years): ₹50–85 LPA Staff / Principal Engineer (8+ years): ₹80–130 LPA

Data Engineer:

• DE-1: ₹12–22 LPA
• DE-2: ₹22–45 LPA
• Senior DE: ₹40–70 LPA

Product Manager:

• APM / PM-1: ₹20–35 LPA
• PM-2 / Senior PM: ₹35–70 LPA
• Group PM / Director: ₹65–115 LPA

Compensation structure at Groww:

• Base salary: 60–70% of total CTC
• Variable: 15–20% (performance-linked)
• ESOP: 15–25% for senior roles (pre-IPO; liquidity dependent on IPO or buyback)
• Groww has done at least one ESOP buyback for employees: better than most pre-IPO companies

Negotiation at Groww: Unlike IT services companies, Groww negotiates on ESOP and joining bonus more readily than on base. If you have a competing offer from Razorpay or Swiggy, it is the single most effective negotiation lever.