Instacart Analytics Engineer interview experience
I went through the whole Instacart Analytics Engineer interview process. Overall it covers SQL + Analytics/Business Case + Data Modeling + Behavioral/Experience. Sharing the specific process here so any sisters preparing for a similar role can use it as a reference.
1. Screening: Fundamentals + Experience Walk-through
The first round was a screening with the recruiter / hiring team. It was not especially technical, more about:
- Going through my resume and past work experience
- Talking mainly about the projects I had done before, the scope I owned, and how I worked with business teams
- Why I wanted to do Analytics Engineering, and why I was interested in Instacart
I suggest preparing every project on your resume ahead of time. Basically anything you wrote on your resume can be followed up on.
2. SQL Coding: 1 hour / 7 questions
The second round is the one I think you really need to practice for beforehand.
The format was roughly 7 SQL questions in 1 hour, getting progressively harder. The earlier questions are fairly basic and test SQL fundamentals; the later ones start to involve:
- Multiple tables / JOIN
- GROUP BY / aggregation
- Subquery
- CTE
- Window functions
- More complex multi-step data transformation
In my case I ran out of time on the last question, so I did not fully finish it.
Practice questions where one CTE builds on the next one in particular, because the later questions are clearly more complex than the earlier ones.
I also strongly recommend practicing with a timer. 7 questions in 60 minutes means the average time per question is really tight, and you cannot get stuck on one question for too long.
3. Senior Team Member: Experience + Behavioral
The third round was a conversation with a senior team member. The share of technical coding dropped a lot, and it was mostly behavioral questions:
- Past work experience
- Project deep dives
- Behavioral questions
- Collaboration
- Stakeholder management
- How to handle ambiguous requirements
- How to work with different teams such as Data Science / Product / Engineering
- How to handle disagreements
- How I solved a project that ran into difficulties
4. Final Loop: 3 rounds
The final loop had 3 interviews in total, and each one focused on a fairly different direction.
Principal Product Manager: Behavioral
The first was with a Principal Product Manager, mainly behavioral / product sense / collaboration. They cared a lot about whether you, as an Analytics Engineer, can truly understand the business problem rather than only being responsible for writing SQL / building data pipelines. I suggest preparing:
- How to turn a business question into a data problem
- How to judge whether an analysis has business impact
- What to do when a stakeholder's request is unclear
- What to do when different stakeholders have conflicting requests
- How to prioritize multiple projects
- How to explain a complex data problem to a non-technical stakeholder
It is best to show that you proactively understand the business, challenge requirements, put forward your own suggestions, and ultimately drive the decision.
Case Study
The second round was a case study with two interviewers together. The case study was roughly about: something like a new store location / new store expansion question, where you need to analyze from a data and business angle whether a new store location is worth opening.
Senior Data Engineer: Data Modeling
The last round was Data Modeling with a Senior Data Engineer. Overall it felt fairly similar to the delivery / marketplace data modeling you would get at DoorDash. The most important thing is that when you design a table, you state the grain clearly first.
Discussion
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