Video summary

Why GOOGLE Rejected Me twice in Coding Interview (and lessons I learned)

Main summary

Key takeaways

Educational

Main Ideas, Concepts, and Lessons (Prioritized)

1) The speaker’s Google interview experiences (two attempts)

The speaker previously shared that he was rejected twice by Google. In this video, he recounts both attempts and focuses on:

  • What went wrong
  • What he learned
  • How those lessons helped him later succeed at Amazon and Goldman Sachs

2) First Google attempt: how the interviews unfolded

A) Getting an interview after many applications

  • He applied broadly (including 20–30 applications to Google), receiving rejections.
  • Eventually, a recruiter reached out (via LinkedIn message) and initiated the process.

B) Phone/telephonic round (45 minutes)

  • Google uses elimination-style rounds.
  • He was asked a monotonic stack question:
    • He explained his approach clearly.
    • He discussed time complexity and space complexity.
    • Follow-ups were solved and coded.
  • Result: the recruiter said the telephonic round went correctly.

C) Onsite round structure (4 onsite rounds, each 45 minutes)

  • Google onsite pattern described:
    • 4 onsite rounds total
    • 3 are DSA
    • 1 is behavioral (“Googly/behavioral”)

Onsite 1 (DSA): reduced to topological sort

  • Solved the initial question quickly.
  • Follow-up: constraints changed; he had not seen it before.
    • He outlined/reduced an algorithm but did not finish coding completely.
  • The interviewer seemed satisfied enough.

D) Onsite 2 (DSA): major mistake—assuming the wrong pattern

  • He received a problem involving ranges.
  • His mistake:
    • He automatically assumed it was a segment-tree/segmentation-style problem.
    • He explicitly indicated he thought it looked like segmentation.
  • The interviewer corrected:
    • It did not look like segmentation
    • It could be solved without it.
  • He pivoted and coded the correct approach (plus follow-ups).
  • Key takeaway from feedback:
    • The interviewer’s notes indicated he was going down the wrong direction initially.
    • He felt the discussion ideally could have shifted in 3–4 minutes, but his early assumption wasted time.

E) Advice derived from this mistake (general interview strategy)

  • Don’t “hard-wire” patterns:
    • “Ranges ⇒ segment-tree” is not always true.
    • “If k elements are extracted ⇒ heap” is also not universally correct.
  • Recommended approach:
    • Spend 1–2 minutes understanding.
    • Consider 2–3 approaches before committing.
    • Don’t panic while thinking—being briefly quiet is acceptable.
    • Avoid expressing uncertainty in a way that forces the interviewer to change the question.

F) Onsite 3 (behavioral/“Googly”): not prepared

  • He assumed behavioral would be easy and did not prepare properly.
  • He describes his behavioral answers as:
    • broken / half-baked
    • lacking structured impact
  • Realization:
    • Behavioral rounds at top firms matter.
  • He recommends preparing STAR format stories with clear results.

G) Onsite 4 (DSA): mistake in time complexity

  • He faced a hybrid graph + tree type problem.
  • He said the algorithm conceptually sounded best, but:
    • His time complexity estimate was wrong
  • Lesson:
    • During practice, be able to justify time complexity correctly, especially for:
      • graph/tree problems
      • cases where recursion/visits may stop early
  • After all four rounds:
    • He was put on hold for ~3 months (team matching), then ultimately rejected.

3) Recovery strategy after the rejection: how he improved

A) “Cool-off” + intensified daily preparation

  • Google’s cool-off was 6 months.
  • He built a daily DSA routine:
    • Used his “personal DSA channel”
    • Solved LeetCode daily
    • Focused on new patterns each day
  • He achieved a 212-day streak (first time he had such a streak).

B) Biggest change in practice: stop practicing randomly

  • Previously, he tried to guess what Google asks.
  • This time, he directly studied:
    • “recently asked questions” (from sources like LeetCode/Kodolio-style summaries)
  • He believed this helped him see:
    • updated patterns
    • what’s actually currently being asked
  • Philosophy:
    • Random practice can solve problems that aren’t relevant—wasting time.
    • Practicing likely topics builds confidence and relevance.

C) Re-application strategy + recruiter behavior

  • He noticed that for Google:
    • If an inward recruiter reach-out happens, interview scheduling is more likely.
  • Practical actions he recommends:
    • Apply promptly (1–2 days after applying/when active)
    • Seek referrals or recommendations
    • If a recruiter sees your profile, respond quickly and professionally

4) Second Google attempt: what went better and why

A) Telephonic round (45 minutes, concept checks)

  • He was asked a medium merging-type problem with multiple follow-ups.
  • The interviewer didn’t require full coding everything.
  • Focus was on:
    • choosing correct algorithms
    • understanding what applies vs. doesn’t
    • handling follow-ups
  • He said it went very well.

B) Onsite rounds again: 4 rounds (3 DSA + 1 behavioral)

Onsite 1 (DSA)

  • Asked about CPU scheduling using a heap
  • Follow-up involved greedy
  • He used prior pattern exposure to answer confidently.
  • He coded and explained time/space.

Onsite 2 (DSA)

  • Asked a graph / multi-source BFS-style question
  • Follow-up included Dijkstra-like reasoning
  • He recognized the pattern from recent practice and executed quickly.

Onsite 3 (behavioral/Googly)

  • Fully prepared with:
    • structured STAR stories
    • impact/result explanations
  • He felt it was his best.

Onsite 4 (DSA): major mistake—confidence collapse on unfamiliar question

  • Problem: image/lines-based structure:
    • design a data structure to store an image made of horizontal/vertical lines
    • compute total squares formed
  • Mistake:
    • He had never practiced this type
    • He became extremely nervous because he expected the same “selection rule” to apply
    • For the first 5–10 minutes, he froze mentally
    • Confidence dropped to zero and he worried he’d be rejected again
  • He recovered:
    • accepted the situation
    • chose a brute-force direction to show progress

C) Key emotional lesson: “wishful thinking” harms performance

  • Biggest conclusion:
    • Don’t enter interviews with the mindset that you must succeed.
  • Over-optimism (“I will get selected”) becomes fragile when something unfamiliar appears.
  • Better mindset:
    • accept rejection is possible
    • take a deep breath
    • focus on producing an approach and partial solution
  • He estimates he wasted ~10 minutes mentally.

5) Final outcome and overall message

  • After the second attempt, he later learned:
    • he was rejected again
  • However, he later succeeded at:
    • Amazon SD2
    • Goldman Sachs
  • Overall message:
    • hard work doesn’t go to waste
    • persistence and learning from mistakes matters

Methodology / Instruction List (Explicit, Step-by-Step When Given)

A) Interview pattern-handling guidelines (avoid early wrong assumptions)

After reading the problem:

  1. Spend 1–2 minutes understanding.
  2. Avoid immediately concluding it matches a known pattern.
  3. Generate options:
    • think of 2–3 possible approaches
  4. Commit only after you’ve chosen a direction.
  5. Don’t panic if you’re quiet briefly:
    • interviewers don’t judge calm thinking time.
  6. Avoid hard-wiring pattern rules, e.g.:
    • “Ranges always ⇒ segment tree/segmentation”
    • “k extraction always ⇒ heap”

Use the problem’s structure to decide, not a fixed template.

B) Behavioral interview preparation guidelines

  • Prepare STAR-format stories:
    • situation
    • task
    • action
    • result/impact
  • Don’t treat behavioral as secondary at top tech firms.

C) Time complexity practice rule

For every practice problem:

  • know the algorithm
  • be able to justify the correct time complexity

For graph/tree problems:

  • analyze whether traversal visits:
    • the entire structure, or
    • only part of it (recursion/DFS may terminate early)
  • compute complexity accordingly.

D) Preparation strategy during the cool-off period

  • Use a daily routine:
    • solve DSA problems every day
    • maintain a long streak (he mentions 212 days)
  • Practice relevance:
    • prioritize recently asked questions
    • build pattern awareness from updated lists (last ~45 days / ~6 months window mentioned)
  • When applying:
    • apply promptly
    • pursue referrals/recommendations
    • respond quickly when recruiters reach out

E) Mindset strategy during unfamiliar last onsite questions

When you see something you haven’t practiced:

  • don’t assume the interview is over
  • take a deep breath (he recommends ~1 minute calm)
  • ask: what’s the worst case? (rejection is possible, but other outcomes exist)
  • start producing an approach even if brute-force:
    • decide quickly
    • code enough so the interviewer sees progress

Speakers / Sources Featured

Speaker

  • Siddharth (referred to as “Siddharth” repeatedly; “Baba” is used as address)

No other distinct speakers are featured in the subtitles (interviewers and recruiters are referenced as roles, not by name).

Sources/Tools Mentioned

  • LinkedIn
  • LeetCode
  • Google job application / recruiter outreach
  • “Kodolio” (used as a place to view updated/recent questions)
  • Google/HR/interview round types (telephonic, onsite, behavioral)

Original video