Analytics team career map at Wise

Career maps are a tool we use to map out all the different levels within a team. They give clarity on what we expect at each level and help our people know how they can progress in their team. They also help us evaluate impact and pay our people consistently and fairly.

We’re sharing our Analytics team levels and salaries to give you an idea of where you might fit, what’s expected at each level and how you can progress at Wise. 

While career map frameworks are a useful guideline, they should never replace one-to-one career development and coaching conversations. All our employees go through an annual 360 feedback review, where we refine our individual development plans. This framework helps our Product leads have more structured conversations with their team members about progression and understand what they need to do to increase their impact on our mission.

When you’re thinking about your skills in relation to these levels, always remember it’s not a tick box exercise, but rather a guide to show the kind of impact we expect from our people as they progress in their journey at Wise. Different roles and teams have varying expectations on certain areas, but on the whole these expectations are common across all roles.

There are two components the Analytics career map: 

✅  Responsibilities: They determine your level and show the expectation that impact increases as you go through the levels

🛠  Skills: Guidance as to what should help you deliver the responsibilities

In every quarterly planning sprint, you will also set:

Objectives: These are specific individual Objective Key Results (OKRs) for a subset of your responsibilities

Glossary of Wise Analytics jargon

  • Tribe: Here we mean either an official collection of teams
  • Team: A smaller unit within a Tribe
  • Cross-team: Involving multiple teams in the same tribe
  • Cross-tribe: Involving multiple tribes
  • Tribe Analysts: Analysts within a specific tribe
  • Analytics Team: All analysts across all tribes
  • Data asset: Anything that enables analytics: a table, pipeline, set of mixpanel events, lookML model, dashboard
  • Data domain: A set of data that is specific to a particular team or problem – e.g. Payin or Liquidity data. “Owning a domain” means becoming highly knowledgeable on the data and topic, taking responsibility for sharing knowledge, and working with engineers to fix and create new data.
  • KPIs v OKRs: KPIs are usually a set of important numbers to monitor. Each quarter, some of these KPIs may become specific OKRs that are targeted for improvement.
  • Wisers: When thinking about the coaching responsibility for Senior and Lead analysts, the term Wisers refers to Analysts, aspiring Analysts, CS data domain experts, knowledge experts etc.