Journey
Interview Experiences
Real rounds, honest reflections, and lessons from the interview trail — shared to help others prepare.
Amazon
SDE 1 · May 2026 · Did not proceed
Second attempt at Amazon SDE 1. Cleared a detailed OA covering a medium-hard DP problem and a Node.js debugging challenge, alongside a comprehensive Work Style Assessment. Further rounds pending.
DSA
- Q1 (~40 min) — DP: 'Get Minimum Conflicts'. Merge two source-control branches (primary, secondary) preserving relative order while minimising inversions (lower-priority commit before higher-priority). dp[i][j] = min conflicts after consuming i chars from primary, j from secondary. Precompute conflict contributions to reduce O(n³) → O(n²). Difficulty: Medium-Hard.
- Q2 (~60 min) — Node.js Debugging: fix bugs in a partially implemented Express project so all 6–8 test cases pass. Bugs: userId read from req.body instead of req.user.id, field name mismatches in DTOs (commentText vs text), incomplete object construction in service calls, wrong argument format from controller to service. No algorithms — pure request-flow tracing.
Behavioral / LP
- Work Style Assessment (~60–80 questions): 4 scenario-based sections, 15–20 Likert-scale ratings each. Evaluated against Amazon LPs — Customer Obsession, Ownership, Dive Deep, Bias for Action, Earn Trust, Deliver Results, Learn and Be Curious.
- Behavioral Questionnaire (10–15 min): personality consistency checks — 'I enjoy solving difficult problems', 'I prefer working independently', 'I frequently take initiative'. Rated Strongly Agree → Strongly Disagree.
Sequential OA — Question 1 must be saved and submitted before Question 2 unlocks. No toggling back once submitted. Total duration ~100 minutes.
Key Takeaways
- For interleaving DP problems, nail the state definition first before thinking about transitions
- Express.js debugging is about tracing the request lifecycle — middleware → controller → service → DTO
- Amazon's Work Style Assessment is consistent across attempts; study LPs deeply, not just for interviews
- Sequential OAs reward planning — solve, verify, then submit each section deliberately
Amazon
Client Interview via Varite INC · April 2026 · Did not proceed
A single client interview round sourced through Varite INC. Covered a recursive tree pruning problem, a distributed smart locker HLD, and two Leadership Principle questions.
DSA
- Recursive tree pruning: given a nested parent-child JSON structure, remove nodes with null data and no valid descendants. Parent nodes that become empty after pruning should also be removed. Concepts: Recursion, DFS, Tree Traversal, Object Manipulation.
Concepts
- HLD — Distributed Smart Locker System: design a locker network accessible across geographical locations. Discussed system components, locker allocation strategy, availability tracking, scalability, and user access mechanisms.
Behavioral / LP
- Two Leadership Principle questions focused on past experiences, decision-making, ownership, and problem-solving approach.
Started with a brief self-introduction and discussion of previous projects and technical background before moving into the coding and design questions.
Key Takeaways
- For tree pruning problems, process children first (post-order DFS) before deciding if a parent should be removed
- Distributed locker systems need clear answers on consistency vs availability trade-offs across regions
- LP questions via agency rounds are just as rigorous — prepare STAR stories regardless of the interview source
Amazon
SDE 1 · December 2025 · Did not proceed
A four-round process spanning three months — OA in December, two onsite rounds in January, a GenAI virtual round in March, and a Bar Raiser virtual round — ultimately ending in rejection after reaching the final stage.
DSA
- Tree-based problem
- Graph problem
Behavioral / LP
- Multiple Leadership Principles (LP)-based questions
DSA
- Remove K consecutive identical characters from a string
- Minimum cost to color N cakes using 3 colors (DP)
Behavioral / LP
- Delivered under a tight deadline
- Went beyond responsibilities and got recognized
DSA
- Merge two sorted linked lists (framed in a real-world scenario)
- Minimum length subarray with sum ≥ target
Behavioral / LP
- Solving a problem with an out-of-the-box approach
DSA
- Design a system to delete personal information from records (linked list approach)
- Split large-scale data into two parts efficiently (optimized single-pass)
Concepts
- Use cases of GenAI on large datasets
- Limitations of GenAI — hallucination, cost, data dependency
- Deep dive into one of my projects, architecture, and contributions
Behavioral / LP
- Career decisions and recent gap explanation
Behavioral / LP
- Deep behavioral questions with multiple follow-ups
- Ownership, decision-making, and clarity of thought
- How you think, not just what you know
Heavily LP-focused. The Bar Raiser evaluates cultural fit and long-term potential above everything else.
Key Takeaways
- Amazon's LPs aren't just interview prep — internalize them as a decision-making framework
- STAR format matters, but the depth of reflection matters more in a Bar Raiser
- Reaching the final round without converting is still meaningful progress
- Preparation for DSA should stay sharp across the entire multi-month timeline
Personal Reflection
“This one hurts more than I expected. After going through all four rounds and reaching the Bar Raiser stage, there was a real sense of hope — that after months of preparation, learning, and a long phase of unemployment, things might finally fall into place. But sometimes, even when you come close, it doesn't convert. This setback hit hard. It shakes your confidence, and for a moment, the motivation dips. That said, I know this journey isn't over. There's still more to learn, improve, and come back stronger. On to the next opportunity.”
SAP
CAP Developer · July 2025 · Did not proceed — internal re-interview policy
Two strong technical rounds covering OOP, core and advanced JavaScript, DSA, and SAP ecosystem concepts. Rejected due to an internal policy — I had interviewed with the same team within the previous six months.
DSA
- Checking balanced parentheses
Concepts
- OOP concepts and fundamentals
- Core JavaScript — hoisting, I/O behavior, basics
Behavioral / LP
- Discussion about internship experience at SAP Labs and work done there
No deep CAP hands-on experience at the time, but the discussion went well and I was shortlisted for Round 2.
DSA
- Array-based problem — deep dive into approach, edge cases, and optimization
Concepts
- Advanced JavaScript — Event Loop, Closures, I/O questions, fetch API
- SAP BTP, HANA, CAP exposure, application architecture at SAP
Two interviewers. One mentioned being impressed with my depth in JavaScript, which was very motivating.
Key Takeaways
- Deep JavaScript fundamentals — Event Loop, closures, async — are high-signal in frontend-heavy roles
- Prior internship experience is a double-edged sword: it gives context but also creates re-interview policies
- Always ask HR upfront about cooldown periods if you've interviewed at the same company before
Personal Reflection
“Although the result wasn't in my favor, the experience reaffirmed my strengths in JavaScript, problem-solving, and SAP ecosystem understanding. Grateful for the learning, the conversations, and the growth that comes with every interview.”