Everyone obsesses over the technical interview. SQL, Python, statistics, case studies — there are entire books written on how to prep for these. And yes, you need to know the technical stuff. But here's what I've seen first-hand at Amazon, and what candidates consistently miss:
Big tech doesn't just hire skill. It hires for signal. They want to know: how do you think? How do you behave when things go wrong? Do you take ownership? Do you move fast? Can you work with ambiguity?
This post is about those signals — and how each major tech company evaluates them differently.
Leadership Principles-driven. Every interview question maps back to one of 16 LPs. Culture fit is weighed as heavily as technical ability.
Googleyness + technical depth. They want curiosity, intellectual humility, and strong coding/stats fundamentals.
Product sense + ownership. They evaluate how well you connect data to business impact — and whether you can move fast with ambiguity.
AMAZON: THE LEADERSHIP PRINCIPLES
If you're interviewing at Amazon or AWS, you need to understand one thing above everything else: the 16 Leadership Principles are not just wall decorations. They are the actual framework Amazon uses to hire, promote, and evaluate every single employee — from interns to VPs.
Every behavioral question in an Amazon interview is designed to test at least one LP. Your interviewer has been assigned specific LPs to probe in your conversation. They're taking notes. After you leave, they'll grade you on each one.
I've sat in debrief sessions where candidates who aced the SQL round didn't get offers because they couldn't demonstrate Ownership or Bias for Action in their behavioral answers. The LPs are that important. Prep them as seriously as you prep your technical skills.
The 16 Amazon Leadership Principles
Start with the customer and work backwards. Always earn and keep their trust.
Act like an owner. Never say "that's not my job." Think long-term, not just your team's metrics.
Find new ways to do things — and make complexity simpler, not more complicated.
Good judgment. Diverse perspectives. Know when to trust your instincts and when to question them.
Never stop learning. Be genuinely curious about new possibilities and ideas.
Raise the performance bar with every hire. Develop others. Coaches create coaches.
Relentlessly high standards — even when others think they're unreasonably high.
Bold direction inspires results. Think differently. Look around corners.
Speed matters. Many decisions are reversible. Don't wait for perfect information.
Do more with less. Constraints breed creativity. Don't spend for the sake of spending.
Be candid and self-critical. Benchmark yourself and your teams against the best.
Stay connected to the details. No task is beneath you. Data over anecdote, always.
Challenge decisions you disagree with — respectfully. Once decided, commit fully.
Focus on the right inputs and deliver with quality and timeliness. Rise to the occasion.
Work to create a safe, productive, and empowering environment for others.
Use Amazon's scale for good. Act with humility. Be aware of your impact on the world.
How to answer LP questions as a junior candidate
The most important thing to know: as a junior candidate, Amazon scales their expectations. They're not expecting you to have led a team of 50 engineers. They want to see LP behaviors in the context of your school projects, internships, part-time jobs, or even personal side projects.
Use the STAR method for every behavioral answer — Situation, Task, Action, Result. Be specific. Numbers matter. "I improved query performance" is weak. "I reduced query runtime from 4 minutes to 30 seconds by rewriting the join logic and adding a partition key" is strong.
| STAR Step | What to Cover | Common Mistake |
|---|---|---|
| Situation | Set the scene briefly — what was the context? | Spending too long here. Keep it to 2-3 sentences. |
| Task | What was your specific responsibility or challenge? | Being vague. "I had to help the team" is not a task. |
| Action | What did YOU do? Use "I", not "we". | Saying "we" constantly. Interviewers want to know your contribution. |
| Result | What was the outcome? Quantify it if possible. | Forgetting the result entirely, or leaving it vague. |
Prepare 6–8 strong STAR stories before your Amazon interview. Each story should be flexible enough to map to multiple LPs. You'll be able to reuse them across different questions — just shift which part of the story you emphasize depending on the LP being tested.
GOOGLE: GOOGLEYNESS + TECHNICAL DEPTH
Google's evaluation framework is a bit different. They look for what they call "Googleyness" — a combination of intellectual curiosity, comfort with ambiguity, collaborative nature, and a genuine passion for the problem, not just the paycheck.
For data roles specifically, Google also places a very high bar on statistics and probability. This trips up a lot of candidates who only prepped SQL. Expect questions about A/B testing, confidence intervals, p-values, and experimental design — explained out loud, not just coded.
What Google data interviews cover:
Google interviewers fill out a standardized feedback form after each session. For junior engineers, a hire recommendation requires positive signals across nearly all evaluation areas — one bad round can sink an otherwise strong performance. Consistency across rounds matters as much as any single brilliant answer.
The behavioral component at Google focuses on how you've demonstrated leadership, collaboration, and intellectual honesty. They want to see that you can disagree respectfully, change your mind with new evidence, and still deliver. Classic "Googleyness" questions include: "Tell me about a time you failed" or "Describe a situation where you had to work with someone you disagreed with."
META: PRODUCT SENSE + OWNERSHIP
Meta (Facebook) has a distinct interview style that surprises a lot of candidates. Yes, they test SQL. Yes, they test statistics. But the round that most people underestimate is the product sense and analytical reasoning round.
At Meta, data analysts and scientists are expected to think like product managers. You need to be able to take an ambiguous business question — "How would you measure the success of Instagram Stories?" — and break it down into metrics, hypotheses, and analytical frameworks on the spot.
What Meta data interviews cover:
Meta's behavioral questions focus heavily on ownership. Their core values — "move fast," "be direct," "focus on long-term impact" — show up in how they assess you. They want to see that you've driven projects end-to-end, pushed back on stakeholders with data, and taken initiative beyond your job description.
For Meta, prepare 4–5 stories that show you driving outcomes — not just completing tasks. The difference is: "I built a dashboard" vs "I noticed our team had no visibility into X, proposed a solution, built the dashboard, and it became the source of truth used in weekly leadership reviews." That second version shows ownership. That's what Meta wants.
WHAT ALL THREE HAVE IN COMMON
Despite their differences, Amazon, Google, and Meta are all looking for the same underlying thing in junior data hires: evidence that you can grow.
They're not expecting perfection. They're investing in potential. Here's what moves the needle across all three:
Everything you claim should be backed by data or logic. Vague opinions without evidence will hurt you.
Can you explain a complex analysis to a non-technical stakeholder? This matters more than fancy code.
Know your gaps. Candidates who can articulate what they don't know — and how they'd figure it out — stand out.
Generic answers kill interviews. Every behavioral answer needs a specific story with a real outcome.
Ask good questions. Show genuine interest in the team's problems. Candidates who are curious are remembered.
SQL, basic stats, and Python/scripting are non-negotiable minimums. Window functions and CTEs are expected.
THE ONE THING MOST CANDIDATES MISS
After watching a lot of interviews and debrief sessions, here's the pattern I keep seeing: candidates over-index on technical prep and under-invest in storytelling.
You can write a perfect SQL query and still not get the offer. But if you can clearly articulate a time you took ownership of a messy problem, used data to drive a decision, disagreed with a stakeholder and backed it up with evidence, or delivered results under pressure — you become memorable. You become hireable.
Big tech companies are interviewing dozens of candidates who can all write decent SQL. The ones who get offers are the ones who can tell a compelling story about how they think and operate.
Before your next big tech interview: (1) Write out 6–8 STAR stories from your past. (2) Map each story to Amazon's LPs, Google's Googleyness criteria, or Meta's ownership values. (3) Practice saying them out loud — not reading them. The delivery matters as much as the content.
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