Swiss Re Data Analyst Job Openings 2026 – Apply Soon!

Qualifications

Swiss Re is looking for someone who enjoys working with data and is comfortable wearing multiple hats — from working in raw messy files to shaping dashboards people can actually use. You don’t need years of experience — but you do need curiosity, ownership, and a willingness to learn fast.

Swiss Re is looking for candidates who meet these requirements:

• You’re comfortable with SQL and Python, and you’ve built things using Advanced Excel, Power Query, or Power BI
• You’ve written some DAX logic, or are excited to learn more about how to shape metrics and KPIs
• You like working with big messy datasets and finding ways to clean and align them so others can use them with confidence
• You’re comfortable talking to business users, not just writing code — and can explain your logic without needing to “sound technical”

These are additional nice to haves:

• You’ve written user-defined functions (UDFs) in PySpark to make your transformation logic reusable and cleaner across multiple pipelines
• You follow systematic coding practices — like organizing logic into steps, adding meaningful comments, or handling edge cases cleanly
• You’ve worked with version control (Git or similar), and understand how to manage updates to code or revert changes if something breaks
• You care about performance optimization — like reducing pipeline runtime, minimizing joins, or improving how fast visuals load in tools like Power BI
• Prior experience with Palantir Foundry, or working with finance data, is a big plus
• You’ve worked on projects where you pulled together data from different places and made something useful

Key Responsibilities

• Design and build robust data pipelines in Palantir Foundry — working with large datasets and Foundry Ontology models
• Develop transformation logic using PySpark and Apache-based technologies to enrich, align, and transform raw data from multiple source systems into streamlined data models
• Lead end-to-end data initiatives including data ingestion, transformation, solution development, and advanced AI/ML model deployment
• Drive industrialization by building robust, enterprise-grade data pipelines and AI applications, shaping best practices for AI governance, responsible AI use, and sustainable scaling
• Support and enhance existing reporting platforms, particularly in Power BI, by updating datasets, fixing DAX, or adjusting visuals as per stakeholder needs
• Create new reporting tools or dashboards that help visualize financial and operational data clearly and efficiently
• Automate manual processes through innovative data flows, transformation logic, or re-usable queries
• Take ownership of reporting problems with a solution-first mindset — diving in to understand why issues occur and how to fix them effectively