Senior Data Analyst - Product Reliability

About 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

Additional Information

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.

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Company Intel

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