How to Ace Your Interview and Land Your Next Data Role

January 2, 2023

How to Ace Your Interview and Land Your Next Data Role

Are you looking to land a new job in the tech industry during these turbulent economic times? It may seem like a daunting task but we've seen some amazing success stories from our Avo users.

By standing out from the competition and showcasing their problem-solving skills with real, practical solutions, these senior analysts and product data managers have secured new roles even during a recession.

So, how can you follow in their footsteps and land your dream job in data?

It's simple: demonstrate your value to the company by showing, not just telling, your interviewer what you can do.

In this post, we'll share with you exactly how to do that, as well as provide an ROI calculator to help you negotiate a better salary. Don't let a challenging job market hold you back – use these tips and tools to stand out from the crowd and secure your dream role.

Overdeliver on the test cases by demonstrating  how to improve data quality at scale 

Test cases shared during the interview process are usually designed to see how you’d approach a problem. This presents an opportunity for you to demonstrate your capability by leveraging modern data tools instead of spreadsheets.

If you aren't asked for a test case, then make your own. Simply ask your interviewer if they’d like a demonstration of how you’d tackle their data quality challenges. If you need to align on whether or not data quality is an issue, here are two potential approaches to start the conversation:

  • You were going through their website the other day and noticed the different core use cases the product addresses. Ask if they are tracking the right metrics for these core use cases. Does everyone share the same definition and do they trust the quality of these critical events, or if that’s a pain point for them?

  • You came across an article on [______] industry yesterday, and it talked about how identifying hidden anomalies is a big challenge for [_______] businesses. Ask if that is something they think could be improved?

Make it clear to the interviewer that you are asking these questions because you want to show them how you’d solve problems for them. Remember that few people have ever seen this issue solved well, so you will stand out if your prospective employer trusts you to elevate their data quality and improve data literacy–both of which Avo helps you tackle at scale.

Framing is everything, so our recommendation is to start with a couple of visuals to help frame the business challenge with data quality and typical analytics workflow. The idea here is for you to highlight an intimate problem awareness, which will increase confidence in you to deliver a working solution.

The Avo workflow for your next analytics release (learn more)

At the end of the day, a problem well defined is a problem half solved. And more likely than not, there data is a mess, and a misframing of the problem is slowing progress. Have fun showing them how their world could be made better. Make sure to use the latest tools, visualize your ideas, and walk them through it.

Here are a few ideas to jump start your interview test case plan.

Create a data pipeline flowchart

Use a whiteboard tool like Miro to show the data architecture. Their flowchart template is ideal for showcasing the flow of data in a pipeline. The flowchart will reduce the time it’d take you to explain the flow of data and make it easier for everyone to understand your proposal. You can use flowcharts to validate your own understanding of their current data flow or analytics collaboration workflows, and to facilitate discussion on the most painful problem areas for the business. You can flag how seemingly tiny issues can create bigger problems downstream which waste precious resources. This will help you align the interviewer to some immediate focus areas you could get started on right away, further increasing confidence in you over other candidates.

Build a tracking plan with Avo

Use a tool like Avo to build the tracking plan. Tracking plans built on Avo are visual and interactive. Using us will help you show your interviewer exactly why and how you plan to track events and roll out a data governance operating model. You will be able to show how you’d build, track, and ship product metrics on an analytics governance tool. Remember that this is how our users are landing jobs in today’s climate–impressive! 

A standard tracking plan built with Avo (Source:

When demonstrating with Avo, avoid jumping randomly between screens, and instead, show complete end-to-end use case workflows, making sure to pause to highlight what makes the features valuable and why it fills their current technology gap. Here are a few ideas for workflows that generally resonate in our sales conversations:

Metric first data design: tracking for sake of tracking does not help the business and just promotes poor data culture. You could show how best practice is to start alignment around needed measurements. We recommend the purpose meeting for this and using Avo metrics. If you ever get asked why to start with metrics, you can highlight that it’s not mandatory, but that two possible use cases for it include:

  • Enabling self serve by mapping events to metrics
  • Improving event schema design and decreasing duplicated events by establishing clarity on needed measurements.

Branched workflows: Gsheets don’t scale. Period. And whenever you have multiple product teams working with continuous deployment, where everyone is collaborating in a Gsheet, Notion, or confluence. Data gets messy fast. Leading to out of date documentation and broken implementation. Try highlighting how Avo solves this with branched workflows, making it easy and safe for anyone to work on their tracking, requesting reviews from peers, and avoiding data debt. Make sure to demonstrate an end-to-end tracking update. Including adding a new event, so you can demonstrate schema warnings, how to add screenshots, source specific properties, and the speed of property groups. It’s also helpful to highlight how combined this helps democratize best practice data design.

Configurable schema policy & data design systems: Before branches get merged, Avo automatically audits tracking plans for common data debt like avoiding duplicate or rogue schema, or missing descriptions. You can adapt these rules and even enforce standards. So talking through these parts helps create awareness around how you can democratize participation in data design, speed up workflows, and increase data quality.

Integrations & automations: Show how you’d enable Slack alerts on schema violations, and make use of publishing to sync your schema designs across other tools like Segment Protocols or Mixpanel Lexicon. Pause to make sure that it is understood that these features help reduce data debt, but also help improve data literacy and self-serve enablement by arming people with clear event schema in the analytics platform, which are updated automatically in the background whenever a branch is merged. Magic 🪄. 

There’s a real cost saving return on investment when adopting Avo. To make this easier to highlight, you can play around with our ROI calculator to showcase how hiring you and purchasing Avo can pay for itself many times over. 

Show real-time tracking 

If the data pipeline starts on a web page, such as a user making a purchase on a product page, use Dataslayer for the inspection part and show an overview of tracking in real time. Dataslayer monitors the data layers used by all major tag managers and updates you when a new value is pushed. Using this tool will cover the inspection part of the use case, and you’d be able to show your interviewer an overview of the tracking on a web page.

Use examples from your past

While all organizations are different, the challenges they face with data quality are similar. That is why any interview about any data role is bound to have questions about data quality — 77% of organizations face these issues.

When data quality comes up during the interview, ask questions about the current state of data tracking in the organization. Prepare examples of your past experiences improving data quality in your current or previous roles. Since data quality is usually improved by using a tracking plan, educating all stakeholders about data standards, and implementing processes to maintain desired standards in the organization’s ecosystem, focus on those areas to highlight your experience. Share examples from your previous work experience about successful and even failed attempts to improve data quality. Lean into creating a feeling that you’ve been through this journey before, so can guide them through the process. 

Concluding thoughts

To stand out in an interview for a data management or stewardship role, it is important to demonstrate your value to the company by showing, rather than just telling, your interviewer what you can do.

In this article, we’ve elaborated on how to do this, by asking for a real use case before the interview and using a modern data tool like Avo to show how you would tackle the problem.

We also highlighted ways to help you frame the business challenge with data quality and typical analytics workflows, to increase confidence in your ability to deliver a working solution. By overdelivering on test cases or demonstrations with modern data management tools, you can differentiate yourself from the competition and secure your dream role in the tech industry.

Avo helps teams like Adobe, Rappi, Delivery Hero, and more to plan, implement, and verify their analytics events and workflows faster with better-quality data. If you want to join a space for professionals who design, create, or consume, we’d love for you to join our Slack channel.