What exactly do product analysts do?


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Put simply, analysts at Wise help teams solve customer’s problems by leveraging data analytics.

We’re hiring. Check out our open roles.

Product analysts at Wise cover a wide scope as they own the entire customer problem end to end – from deciding what to work on, building necessary data pipelines, analysing and visualising, and then ensuring that the insights are actioned upon. Analysts at Wise have a broad skill set across product and data, and there is a fair amount of overlap with adjacent fields like analytics engineering, data science, and product management.

The primary responsibilities (and thus skill sets) of an analyst are:

  1. Helping their team identify and execute towards solving the biggest opportunities – ie, having a strong product thinking mindset.
  2. Owning the team’s data products and analysis – including pipelines, dashboards, and source of truth tables – ie, having the necessary technical and statistical skills.

Key attributes

There are certain fundamental attributes we look for in analysts –

  1. Bias to impact – Every analysis should have a tangible end customer impact – analysts don’t spend time on problems that are merely ‘interesting’. They are experts at prioritising between speed versus accuracy.
  2. Ownership and autonomy – analysts own the entire problem space end to end, not just the data part of it. They find out the biggest problems to work on and inspire teams to solve those problems. They are not ‘SQL/Looker machines’ who just output numbers on requests from stakeholders.
  3. Curiosity – analysts are deeply curious about how customers are using our products, and what their pain points are. They obsess over identifying key levers of growth and building models to explain how they interact with each other.
  4. Storytelling – analysts inspire their teams to action by telling a story from their findings, by helping them understand the magnitude and nature of the problem.

Our tech stack

Our analytics database is currently hosted on Snowflake, and we have an internally developed, open source replication tool called PipelineWise (managed by a central analytics platform team) that feeds data to our analyticsDB.

We follow ELT – with the Extract and Load parts (along with obfuscation of PII data) managed through PipelineWise, and the Transform layer done through Airflow. Analysts are able to configure the data pipelines fairly independently of platform teams.

SQL is heavily used, and we expect analysts to be fluent in it. Basic Python is nice to have – we tend to use it for more advanced modelling, data visualisation and data engineering tasks.

Our BI and dashboarding tool is Looker, and 70% of all Wisers interact with it every week – the analyst team has a lot of data hungry consumers!

For front end event tracking, we use Mixpanel, and GitHub to peer review and version control every script that goes into production.

Finally, we’re currently in the process of adopting dbt – something that a lot of us are very excited about!

How is the team organised?

At Wise, we have autonomous product teams that are formed around a core customer problem. A collection of teams working on related problems form a ‘squad’, and a collection of squads form a ‘tribe’.

In line with our autonomous culture, whilst we have an Analytics team at Wise, we are mostly working in an embedded model, with analysts embedded within product teams.

Each analyst has a ‘home’ product team in which they are strongly embedded. Analysts working in the same squad come together to form an analyst team, and report into an analyst lead for the squad. While analysts have home teams, they’re encouraged to solve problems of highest impact, and this often means working on adjacent or cross-team topics – e.g. analysts coming together to build a unified segmentation across different products.

The direct reporting line for an analyst is into the analyst lead. The analyst lead supports the analyst’s long term personal growth, and provides coaching and mentorship as needed for day to day execution – ranging from technical guidance, to stakeholder management and prioritisation.

Since the home team is where analysts have their largest impact, the product manager of the team is another key stakeholder. Partnering closely with PMs, analysts develop a deep understanding of the problem space, and ultimately influence decisions that lead to customer impact.


Team culture

There is only one analyst in a team usually, so we have been fairly conscious of creating a culture where there are spaces for the analytics community to come together and learn from each other.

The analyst teams at the squad and tribe level is the primary space where analysts collaborate the most. This includes –

  1. Bi-weekly syncs – to discuss progress on team OKRs, do analytics critiques, brainstorm improvements to data assets etc.
  2. Code reviews – we encourage on PR reviews for all major changes to data assets.
  3. Team OKRs – work on projects that improve the analytics products, team health, knowledge sharing etc.

On the cross team level, we have various events to bring analysts together, including:

  1. Analytics days – a bi-annual conference where all analysts come together.
  2. Hackathons – our legendary full day hackathons.
  3. Brownbags – monthly lunch and learn sessions.

Outside of that, analysts across teams collaborate to produce Mission in Numbers, a monthly newsletter sent to the company updating progress on our mission.

Career Progression

Transparency is one of our core values at Wise, and in line with that, our career map and salary bands are public.

Analysts advance their careers at Wise through increasing their impact and skills. Impact is quite hard to define in a standard way across teams, so we use responsibilities as a proxy to measure it.

Leverage is a big factor within impact, and as analysts grow, we expect them to have increased leverage – either by taking on people management responsibilities, by informal coaching, or by producing work that can be leveraged across the organisation.

Skills are of course important, but ultimately we view it as a means to an end and not as a checkbox to be filled. As long as an analyst is increasing their impact, they would not be blocked by progressing just on account of not having a particular skill on the career map.

We are also firm proponents of horizontal growth – either through switching teams, or by moving across organisations. Given the broad nature of the role, moving into adjacent fields of data science, data engineering, and product management are quite common paths for analysts at Wise.

We also run an Analyst Academy – a program for aspiring analysts to gain the skills and work on real world problems with direct mentorship from analysts.


We are always on the lookout for great analysts to join our team. If working at Wise sounds interesting – do take a look at our open roles and apply!

We’re hiring. Check out our open roles.

We’re people without borders — without judgement or prejudice, too. We want to work with the best people, no matter their background. So if you’re passionate about learning new things and keen to join our mission, you’ll fit right in.

Also, qualifications aren’t that important to us. If you’ve got great experience, and you’re great at articulating your thinking, we’d like to hear from you.

And because we believe that diverse teams build better products, we’d especially love to hear from you if you’re from an under-represented demographic.