Senior Data Analyst - Product Reliability

Wise is a global technology company, building the best way to move and manage the world’s money.

Min fees. Max ease. Full speed.

Whether people and businesses are sending money to another country, spending abroad, or making and receiving international payments, Wise is on a mission to make their lives easier and save them money.

As part of our team, you will be helping us create an entirely new network for the world's money.

For everyone, everywhere.

More about our mission and what we offer.

Wise is on a mission to facilitate borderless transactions – instant, transparent, and eventually free. Behind our seamless customer experience is a complex system of services that process over $100 billion annually for 16+ million customers worldwide. As we scale to serve millions more customers, reliability isn’t just a technical concern, it’s core to customer trust and business performance.

The Platform Engineering Tribe:

We are the backbone of Wise's technical infrastructure, ensuring everything runs smoothly for our customers and engineering teams. Our Reliability squad combines expertise in systems availability and uptime, monitoring performance metrics, and developing robust applications, to create a resilient platform that our customers can depend on 24/7/365. 

Our impact is indirect but critical: when Platform works well, every product team ships faster, safer, and at lower cost. We operate around four north-star KPIs: Productivity, Cost Efficiency, Risk, and Reliability.  This role is responsible for reliability.

The Role:

In this role, you will partner with senior engineering and product leaders to bring clarity to reliability: what it means in practice, how we measure it, and where we need to improve. You will connect low-level system signals: incidents, performance, failure modes, to their impact on customers and the business, and use that understanding to shape decisions.

You’ll play a key role in ensuring reliability is built into how we design and operate systems from the outset, not treated as an afterthought. This includes identifying where we are falling short, highlighting the most important risks, and influencing how teams invest their time between building new capabilities and strengthening existing systems.

Success in this role means that:

• Reliability improves in measurable ways across the platform
• Teams make better, more informed decisions because of your work
• Leadership has a clear, consistent view of reliability and its impact on customers

This is a role for someone who combines analytical depth with strong judgement and a bias for impact: someone who wants to shape outcomes, not just describe them.

About You:

We’re looking for someone who is obsessed with moving metrics, not just analysing them. We care far more about what you changed than what you built.

You might be a strong analyst, product-minded data scientist, or even an ex-engineer, but what matters most is that you:

• Take ownership of outcomes, not just outputs
• Are comfortable operating in ambiguity and shaping problems
• Influence senior stakeholders without formal authority
• Care about real-world impact more than perfect models

How You’ll Work:

• Own the Reliability metrics end-to-end: definition, measurement, targets, and progress
• Partner deeply with engineering: understand systems, incidents, and trade-offs
• Drive decisions: bring clarity to complex problems and push for action
• Balance short vs long-term: incidents vs systemic improvements
• Create leverage: frameworks, dashboards, and narratives that scales

Technical Expectations:

• Excellent SQL proficiency and experience with data visualization tools like Looker, Grafana, Lightdash, or Superset
• Ability to build and manage data pipelines that are modularised and scalable, using tools like DBT and Airflow 
• Familiarity with observability/reliability concepts (SLIs, SLOs, incidents)
• Some experience with Python / data transformation (DBT, etc.) is helpful

This is not a data engineering role, depth in pipelines is less important than impact on decisions.

Why Should You Join Our Team?

• Own a company-level KPI: not a dashboard, but a real outcome
• High leverage: your work affects hundreds of engineers and millions of customers
• Complex, meaningful problems: distributed systems, real-time failures, trade-offs at scale
• Influence without authority: work directly with senior engineering leadership 

Benefits:

• Salary: £60,000-£85,000
• Competitive stock options in a profitable company
• Flexible working conditions tailored to support a balance between work and personal life
• Annual budget for personal and professional development

Please note that Wise does not provide visa sponsorship for this role. Applicants must have the right to work in the UK.

Commitment to Diversity:

Wise is dedicated to fostering a diverse and inclusive workplace. We strongly encourage applications from all backgrounds and are committed to providing equal opportunity in our employment practices.

If you're passionate about using your analytical skills to enhance platform reliability within a leading international financial services company, apply now to join us at Wise!

Good Reads:

• Platform Engineering KPIs
• Scaling our infrastructure at Wise: how we make it work
• Wise's Tech Stack 2025
• Measuring meaningful availability / uptime of Wise
• Why Observability is a must for product engineering teams
• Grafana Mimir Compaction: From Bottleneck to Savings

For everyone, everywhere. We're people building money without borders  — without judgement or prejudice, too. We believe teams are strongest when they are diverse, equitable and inclusive.

We're proud to have a truly international team, and we celebrate our differences.

Inclusive teams help us live our values and make sure every Wiser feels respected, empowered to contribute towards our mission and able to progress in their careers.

If you want to find out more about what it's like to work at Wise visit Wise.Jobs.

Keep up to date with life at Wise by following us on LinkedIn and Instagram.