Are you up for building and optimizing the product development machine of one of the fastest-growing software scale-ups in Northern Europe? Do you have the technical skills and business understanding to bring actionable insights and statistical rigor to the teams that propel us to world domination? Join ZIVVER as a data analyst for product!
ZIVVER is a technology scale-up that provides secure communication software to reduce the risk of data leaks for businesses. We are one of the fastest growing businesses in the Netherlands, having taken less than five years to go from a mere idea to hundreds of enterprise customers, over $15 million raised and 100 employees. We are shooting for world domination, and you can be a part of this!
As data analyst, you are at the core of everything we do in product development at ZIVVER. You help the product development teams build an effective machine for bringing great features from conception to delivery and beyond. To do this, you analyze data to determine which features are most critical to our customers and how they can be most effectively developed. You also cooperate with our engineers on developing data science products for our customers. You work closely with product development to understand their needs, use this knowledge to prioritize your own projects and build the infrastructure that makes your own life easier. In doing so, you work closely with your peers in the data team, who provide you with technical feedback and laugh at your geeky jokes. An awesome role in an awesome company.
A day at ZIVVER
(We are living during a pandemic, so there’s no such thing as a day at HQ. We fully support a work-from-home situation (read as monitors, headphones, webcam, docks, and whatever else you might need). In normal times though...)
You walk into the office with a clear picture of what you want to get done today. You combine driving structural improvements in data infrastructure and analysis with serving the ongoing data needs of product development and need great focus to balance the two.
You start off by responding to feedback from your data team peers on your merge request. You are working on modeling new product analytics data in the data warehouse. This data helps us understand how this new feature is being used, what usage patterns we should optimize for and how this part of the product can be improved further. Having handled the comments, you join the data team stand-up and talk the team through your progress and your plan for the day. You spend an extra 15 minutes giving a junior data team member advice on improving the performance of a churn prediction model that they are working on and also make recommendations on how to structure projects like this more effectively in the future.
Next, you join the weekly product roadmap review to chip in on which features are most valuable to our users. You have discovered that a usage pattern that was previously dismissed as an edge case is actually very common. Based on this insight, the team decides to prioritize a major UX change. You advocate that as part of the development process for this change we run an A/B test to compare the old and new design. After the meeting you take some time to help the product owner use our self-service data infrastructure to determine which browsers we should optimize this feature for.
In the afternoon you continue working on a small proof of concept for a data science product. We are experimenting with using anomaly detection algorithms to identify potential data leaks at our customers. Since the fear of undetected data leaks literally keeps our customers awake at night, this would be a huge win if we can pull it off.
While you are at this, a colleague from customer service asks for your help to use product data to validate a solution to an urgent problem from a major customer. You remember that we did a similar analysis before and luckily took the time to productize it. You help your colleague in record time and just before sprinting back to her desk, she mentions how nice it is that she can do this analysis herself next time.
Near the end of the day, you see that your changes to the data warehouse have been accepted and released. You go home satisfied, knowing that you helped the organization determine what to do and that because of your work on infrastructure, you can do this even better tomorrow.
What will you do?
What do we offer?
What skills and experience do you need?
What kind of person do you need to be?
We need you to fit our company DNA: Smart, Simple and Secure
On top of that, as a product data analyst you need to be...