At a Glance
- Tasks: Drive reliability metrics and influence engineering decisions for a seamless customer experience.
- Company: Join Wise, a global leader in money management and borderless transactions.
- Benefits: Competitive salary, stock options, flexible work, and personal development budget.
- Other info: Diverse and inclusive workplace with excellent career growth opportunities.
- Why this job: Make a real impact on millions of customers and shape the future of financial services.
- Qualifications: Strong analytical skills, SQL proficiency, and a passion for real-world impact.
The predicted salary is between 60000 - 85000 £ per year.
hackajob is collaborating with Wise to connect them with exceptional professionals for this role.
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.
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.
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.
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
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 scale.
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.
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!
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Analyst - Product Reliability in Bristol
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Wise!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Senior Data Analyst - Product Reliability at Wise.
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Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Wise.
✨Apply Directly through Our Website
When you find a suitable opening like Senior Data Analyst - Product Reliability at Wise, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Senior Data Analyst - Product Reliability in Bristol
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Wise, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Wise. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Wise
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Wise!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
