Our data interview process

Our data interview process

At incident.io we’re growing our Data team from 2 to 3 people (see all of our open roles here)! After our recent fundraising, we’ve stepped our growth up a gear and are bringing someone on board to help us with all things data as we scale.

We’ve spent a fair bit of time on our data infrastructure and core models (blog post coming soon 👀) and are now really doubling down on the analytics side of things - so it’s a great time to be joining us!

Interview processes can be opaque, so in line with our blog post on our Engineering recruitment process, we thought we’d do the same for our Data process to lift the curtain a bit on what to expect.

The process is:

  1. Initial call (30 mins)
  2. Technical call (1 hour)
  3. Take home task (2 hours max)
  4. On site (2 hours):
    • Skills interview (45 mins) - We’ll make sure you get a break before the 2nd interview!
    • Culture interview (1 hour)

In this post, I'll go over each interview step and explain why we do it, who you’ll speak to, and how it works. I'll also share some tips on how best to prepare. All of them can be done remotely, although we prefer to bring you on-site to do the final two interviews together which doubles up as an opportunity to meet the team.

We iterate on our interviews in response to how effective the team think they are and the feedback we receive from candidates afterwards — this is a snapshot as of August 2022.

1 - The initial call (30 mins)

Why do we do it?

This is primarily a chance to say hi, get to know each other, and discuss any initial questions you might have about the product, team, or interview process itself. It’s also a chance to discuss expectations around timelines, location, visas and salary upfront.

Who will I speak to?

This call will be with someone from the Talent team.

How does it work?

It’s a 20-30 minute call, done remotely over a Google Meet. It involves:

  • A few questions from us - on your background in analytics & SQL, why you applied to incident.io, and what you want to focus on next in your career
  • A few questions from you - perhaps about the product, the team, our culture, our tech stack or the interview process. Whatever’s on your mind.

Tips on how to prepare

  • Think about whether you want a role in a startup at our stage. If you’re not sure, what questions could you ask us that would help you make that decision? We care a lot about making this a fantastic place to work, but we move fast, there’s a lot of ambiguity both personally and as a business, and things change frequently. It’s not for everyone, and that’s absolutely fine
  • If you want to learn more about us and the role before we chat, ensure you’ve read the job description. If you have time, you might want to check out our blog posts, our changelog, and perhaps the product itself, the documentation or our Slack community. Is this a team you’d want to be a part of, and a product you’d be excited to help build?

2 - The technical call (1 hour)

Why do we do it?

We’re looking for 2 things at this stage - mindset, and technical knowledge.

Mindset is hugely important as in our environment there will be a lot of ambiguity in the problems you have to solve, and lots of scenarios where you’ll have to pick up and use a tool you’ve never come across before. Not everyone will like a scrappy start-up environment and that’s absolutely fine, but it’s a good opportunity to align expectations.

With technical knowledge, we’re looking to see whether you’ve had experience in lots of the data work we do day to day, from building data models through to what tools you use on a frequent basis.

Who will I speak to?

This call will be with either one of us from the Data team.

How does it work?

It’s 1 hour long, and done remotely over a Google Meet.

Trying to recall past examples on the spot isn’t the best experience, and we’re not trying to catch you out in this process. So, to remove any doubt on what you’ll be asked, here’s the list of questions - exactly how we’ll ask them:

  • What's an example of an interesting problem which you've really enjoyed solving?
  • What are the key data tools that you use to get your job done?
  • What’s an example of a data model you’ve had to build from scratch? Why did it need building?
  • What’s the data tool that you use most often? What would you change about it?
  • Talk me through a time you had to quickly get to grips with a tool you hadn’t used before (doesn’t have to be a data tool)
  • Tell me about a time when you had to get something delivered quickly, and what trade-offs you had to make as a result
  • What questions do you have for me about incident.io, the team, or the role itself?

Tips on how to prepare

  • Read through the questions above and think through some examples to run us through, we’re looking for clear structure in your answers
  • Think about how you work best, and what questions you need to ask us to figure out whether incident.io would be the right environment for you to thrive in!

3 - The take home task (2 hours max)

Why do we do it?

As with all recruitment processes, there’s a tradeoff. A take home task gives us a very clear insight into how someone thinks, and is very useful from our perspective. But, you can often be in multiple processes at once and having several 4+ hour take home tasks is not only daunting - it’s a huge time sink.

We made a trade-off, and have made a take home task that we genuinely believe can be done in 1-2 hours.

Fundamentally, the task is “here is a bunch of anonymised data, go find our active user numbers and 1 other interesting thing”. It’s as reflective of our work in the Data team here at incident.io as can be! It’s also a great opportunity for you to get hands on with some of our actual data, and to show off your SQL and analytics skills.

How does it work?

We give you access to an anonymised dataset in Google BigQuery (our reporting database), containing data tables for:

  • Organizations
  • Users (many → one relationship with Organizations)
  • Incidents (many → one relationship with Organizations)
  • Timeline Items (many → one relationship with Incidents), these are basically “things that happen in an incident” such as a slack message, or someone being assigned a role

We give you a Notion doc that runs through the task, what’s expected, and data dictionaries for the above 4 tables (plus a list of the types of timeline items).

The output of the task is a maximum 2 page Google doc - quality over quantity! In this doc we want to see:

  • An update on how our 30 day active user numbers have changed over time
  • 1 other interesting trend you find in the data
  • A “so what” of the analysis - what recommendations can you draw from the above?

We’d also like to see your SQL code that you used to generate any datasets.

Tips on how to prepare

Interview 4 (part 1) — Skills interview

Why do we do it?

At this point we’d love to invite you into our office near Old Street! Our on-site interviews can be broken down into 2 parts: the skills interview and the culture fit.

The skills interview is for you to run through your take home task, and also for us to run through a problem solving exercise that would be very similar in style to the types of thing you’ll work on here at incident.io.

Who will I speak to?

One of us from the Data team, and someone from the wider company.

How does it work?

This interview is 45 minutes long, and usually done on-site in our office. We make this step 45 minutes to give you a bit of breathing room between the 2 interviews.

It’s largely broken down into 2 steps:

  1. (~20 minutes) Running through your take home task and fielding questions from us on your approach, any assumptions you made, and what you’d do next if you had more data and time
  2. (~15 minutes) Working through a hypothetical situation where you’ve been asked to measure the success of a new feature launch, or how effective an existing sign-up funnel step is. We’ll also ask you to think through what data you’d collect and what data models you’d build (assuming this was from scratch). The context of the problem is less important, this is more to look at how you approach an ambiguous problem, how you reason what you should / shouldn’t measure, and how you think in terms of data modelling

Tips to prepare

  • Think through how you would take the results of your take home task to the next step, and what other things you wish you had data for
  • Be prepared to talk through specifics of how you’d measure a new feature launch / sign-up funnel step - common types of metrics, what data you’d collect, and an idea of the data models and their granularity. Whilst it’s valid for you to cover who you’d speak to and more of the process of figuring out what to measure itself, we’d also like to see how you think from a data modelling perspective

Interview 4 (part 2) — Culture interview

Why do we do it?

We've already covered most of what we're looking for in the interview process by this point, but it’s all been focused on Data. There’s a lot more to the team here at incident.io!

This interview is done for everyone we hire, regardless of role, so don’t be surprised if some of the questions seem similar, or you find yourself referencing the same examples. That's ok. This interview is typically going to be with folks outside Data, and it’s a good chance to discuss things from a less technical perspective.

Who will I speak to?

Up until this point, you’ll have met a small number of the incident.io team. However, we have all sorts of amazing people you’ll get to work with if you come here. This is your chance to meet some more of them!

You'll most likely be speaking with two people you’ve not met already, often Stephen (our CEO) and someone from outside of Data.

How does it work?

This interview is more of a two-way conversation and a great chance for you to ask us about things that matter to you. We have a few questions and topics we’d like to cover and we’ll use those as a guide, but we’re expecting you to come with questions of your own, so don’t hold back.

A few examples of topics we might cover include:

  • Motivation - what gives you energy? How can we give you more of that?
  • Feedback - what does great feedback look like to you?
  • Culture - is there anything you love to see from a company culture? Why?
  • Conflict - disagreement and tension can be good, how do we ensure we keep them healthy?

Tips to prepare?

  • Reflect on what you want from your job — are there any examples of things you’ve particularly enjoyed or not enjoyed in past roles? Why?
  • Are there examples of times in your career which were particularly formative? Why?

That's it!

We’ve done our best to make sure we get to know you well and vice-versa, without being too overbearing. As we’re still a very early stage company we’ll be tweaking this process as we go along - we’d love to hear your thoughts on how you find it!

Interested in joining us? Check out our open roles 🙂.

Picture of Jack Colsey
Jack Colsey
Analytics Manager

Operational excellence starts here