Preparation at Wise should focus on blending technical precision with a product-centric mindset. Do not just memorize syntax; focus on how you structure your logic and communicate your findings.

Technical Proficiency – We expect fluency in SQL, Python, and BI tools. You should be comfortable writing clean, efficient queries and performing complex data manipulations under pressure.

Problem-Solving & Case Studies – You will be assessed on how you break down ambiguous problems. When presented with a case, state your assumptions clearly, define your metrics, and explain the "why" behind your proposed solution.

Communication & Stakeholder Management – Data is only as valuable as the action it drives. You must demonstrate the ability to translate technical findings into clear, actionable narratives for product managers and leadership.

Mission Alignment – We are a mission-driven company. You should be able to articulate how your work as a Data Analyst contributes to our goal of making money move "for everyone, everywhere."

The Wise interview process is designed to be thorough, assessing both your technical "hard skills" and your ability to integrate into our multidisciplinary teams. You should expect a rigorous journey that includes screening calls, technical assessments (often involving take-home tasks or live coding), and stakeholder interviews.

The timeline above illustrates the standard progression from initial recruiter screening to final panel interviews. Candidates should interpret these stages as sequential gates; ensure you are prepared for the technical intensity of the mid-process rounds, as these are often the most critical for moving to the final stage.

Technical Assessment (SQL/Python)

We prioritize accuracy and efficiency. You will be evaluated on your ability to write production-ready code.

Be ready to go over:

• Advanced SQL window functions, joins, and subqueries.
• Python libraries for data analysis (e.g., Pandas, NumPy).
• Data visualization best practices for communicating trends.

Example scenarios:

• "Optimize this slow-running query."
• "Write a script to clean this messy dataset."

Case Study & Presentation

This tests your ability to take a broad business question and turn it into a structured analysis.

Be ready to go over:

• Defining key performance indicators (KPIs) for a new product feature.
• Analyzing risk/fraud data to identify emerging patterns.
• Presenting your methodology and trade-offs clearly.

Example scenarios:

• "How would you approach a QBR with a client?"
• "Analyze this dataset and present your recommendations for improving conversion."

Stakeholder Collaboration

We work in cross-functional squads. You must show that you can partner effectively with engineers and product managers.

Be ready to go over:

• Navigating conflicting priorities.
• Influencing decisions without direct authority.
• Translating technical blockers into business-impact terms.

As a Data Analyst at Wise, you will be responsible for scaling our data infrastructure to support regional teams. You will move beyond simple dashboards to build proactive monitoring systems that catch risk and compliance issues before they affect our customers.

Your day-to-day will involve:

• Partnering with FinCrime and product teams to integrate local risk typologies into our global engines.
• Establishing control efficiency frameworks that reduce risk without hindering product growth.
• Collaborating with engineers to ensure data quality and accessibility across our product streams.
• Translating granular metrics into strategic narratives that inform leadership decisions.

We look for "hustlers" who can navigate complexity with limited direction.

• Must-have skills: 5+ years of experience in a data-driven environment, advanced SQL, Python, and experience with BI/visualization tools.
• Product Intuition: A strong analytical mind focused on the fintech ecosystem, particularly in banking, payments, or risk.
• Communication: Proven ability to translate complex data into compelling narratives for non-technical stakeholders.
• Nice-to-have: Experience with Mixpanel, Amplitude, Airflow, dbt, or GitHub.

Q: How long should I spend on the take-home case study? A: While instructions may vary, prioritize quality of insight over time spent. Aim to deliver a clear, actionable recommendation within the suggested timeframe, typically 2–5 hours depending on the specific task.

Q: Is the technical interview very difficult? A: It is rigorous and thorough. Expect to demonstrate your coding skills in real-time or via a structured assessment; focus on edge cases and code readability.

Q: What is the culture like at Wise? A: We value autonomy, directness, and a "mission-first" mindset. We look for individuals who are curious, humble, and willing to challenge the status quo to better serve our customers.

Q: How long does the process take? A: On average, the process can take several weeks depending on scheduling. We aim to provide timely feedback, though we encourage you to follow up if you have not heard back within the expected window.

• Focus on the "Why": In every analysis, explain not just what the data says, but what action the business should take as a result.
• Be clear on assumptions: If a problem feels ambiguous, state your assumptions upfront. This shows you can handle real-world uncertainty.
• Prepare your own questions: Use interviews to learn about the team’s current challenges. This demonstrates your genuine interest in the role.
• Stay mission-aligned: Always keep the customer’s perspective in mind. How does your analysis make their life easier or save them money?

The Data Analyst role at Wise is a high-impact position that sits at the intersection of technology, finance, and product strategy. Success in this role requires a blend of technical mastery, sharp business intuition, and a relentless drive to solve problems for our global customer base.

By focusing your preparation on structured problem-solving, clear communication of technical insights, and a deep understanding of our mission, you will be well-positioned to navigate our interview process. We encourage you to use the insights here to refine your preparation. Your potential to contribute to our mission is significant, and we look forward to seeing how you can help us continue to build money without borders.