How to Crack Product-Based Company Interviews

Product-based company interviews have a reputation for being mysterious and brutal. In reality, the large product companies publish a lot about how they interview, and most candidates do not read it. This guide pulls together what Amazon and Microsoft say on their own careers pages, with a focus on software and data roles, and turns it into a plan.

How the process is structured

Most product companies follow a similar path: an online assessment or recruiter screen, one or two technical screens, then a series of interviews on one day, often called a loop or onsite.

Amazon describes its loop as a set of individual meetings with current employees, each assessing different aspects of your skills and experience, so the team builds a well-rounded picture of how you would perform (Amazon: interview loop). Microsoft says its later steps vary by role and may include meeting future teammates and cross-functional colleagues, coding exercises, portfolio reviews or other work samples (Microsoft: interview tips).

The key consequence: every interviewer in the loop is assessing something specific. Your job is to give each of them clear evidence on their topic.

What data roles are tested on

Amazon's preparation page for business intelligence engineers is unusually specific. It describes the role as defining KPIs, automating data pipelines and building reports and dashboards, and says its BIEs understand statistics, data warehousing and ETL and are proficient in SQL, including advanced SQL on ambiguous data. Its online assessment has three required parts: a SQL Challenge, a section on working with SQL, and a work styles section (Amazon: BIE interview prep).

That pattern is common across product companies for data roles:

• SQL, tested hard: joins, window functions, aggregation with edge cases, and queries on messy data.
• Data modelling and pipelines: star schemas, incremental loads, and data quality.
• Metrics and product sense: how you would define and measure a feature's success.
• Statistics: experiment design, significance and common pitfalls.
• Behavioural questions about ownership, ambiguity and influence.

Software roles add data structures and algorithms rounds and, at senior levels, system design.

How technical answers are judged

Microsoft spells out what it wants when you solve a problem: ask clarifying questions, state your assumptions, and talk through your approach, including the tools you would use and why. It says it wants to see not only what you know but how you think.

In practice that means:

1. Clarify first. "Can a customer have multiple accounts? Should cancelled orders count?"
2. State a plan before writing code or SQL.
3. Start simple, then optimise.
4. Test your own answer with an edge case.
5. Explain trade-offs: speed against readability, accuracy against cost.

Behavioural rounds carry real weight

Both companies recommend the STAR structure, situation, task, action and result, for behavioural answers. Amazon frames its questions around its Leadership Principles, such as Customer Obsession and Ownership. Read the principles of any company you interview with, and prepare stories that show the ones relevant to the role. Behavioural interview questions for data roles explains how these answers are scored.

Prepare six to eight stories that each show something different: a project you owned end to end, a disagreement handled well, a mistake and what you changed, working with ambiguous requirements, a result you measured, and influencing without authority. Keep the result part concrete: numbers, decisions changed, time saved.

A six-week preparation plan

Throughout, practise out loud. Product company interviews reward clear reasoning spoken as you work, which is a different skill from solving problems silently.

On the day of the loop

A full loop is tiring: several interviews back to back, each with a fresh interviewer who has not seen how the previous ones went. Treat each one as a new start. If one round goes badly, do not carry it into the next, because the next interviewer is assessing something different and will only see what you show them. Eat and drink between sessions if there is a break, keep your stories and a few SQL patterns on one page to glance at, and use the time at the end of each interview to ask a real question about the team's work. Interviewers notice candidates who are curious about the problems they would be solving.

Common mistakes

• Skipping the published material. The company's own preparation pages tell you what is tested.
• Over-preparing algorithms for a data role and under-preparing SQL and metrics.
• Stories without results, or stories where "we" did everything and your own role is unclear.
• Jumping into code without clarifying the problem.

For the bigger picture of moving from an IT services background, see from service to product company. For question practice, see SQL interview questions.

SQL, modelling and metrics practice for data interviews is part of the Data Analytics course.

Sources

• Interview Loop (Amazon Jobs) — Amazon
• BIE Interview Prep (Amazon Jobs) — Amazon
• Interview tips (Microsoft Careers) — Microsoft