Analytics Engineer

At Swiss Re, we're on a mission to make the world more resilient, and we need bold, curious minds to help us get there. If you thrive at the intersection of data, AI, and real-world problem solving, this is your opportunity to work on the bleeding edge of technology and make a tangible impact on how decisions are made across our business.

About the Role

As an Analytics Engineer, you will be the critical bridge connecting our transactional systems with powerful analytical platforms, completing the loop from data capture to decision-making. You'll fuse large volumes of structured and unstructured data from our data lake with cutting-edge AI models and custom code, serving up queryable, high-quality, permissioned APIs that inform real decisions taken by real users.

This is a role for someone who loves applying analytical thinking and programming expertise to solve complex, meaningful challenges in a fast-moving environment.

Key Responsibilities

• Design and implement Agentic AI workflows that power intelligent, automated decision-making across business applications.
• Develop evaluation test suites to rigorously validate model correctness and ensure reliable, trustworthy outputs.
• Create safety guardrails to enable responsible, secure access to AI models in production environments.
• Write and review high-quality analytical Python code, ensuring appropriate architectural patterns and best practices are consistently applied.
• Manage and optimize Spark build pipelines to support efficient, large-scale data processing.
• Own operational production stability, including incident handling, application monitoring, and end-to-end testing to maintain platform reliability.
• Collaborate cross-functionally with product owners, engineers, and domain experts to translate business needs into scalable analytical solutions.

About the Team

Our Corporate Solutions Platforms team is a group of passionate technologists who believe that great software is built on a foundation of technical excellence and genuine collaboration.

We design and implement carefully crafted applications that consistently exceed user expectations, working in close partnership with product owners to deliver real value. Quality and stability are at the heart of everything we do. We hold ourselves to high standards, lead by example, and embrace continuous improvement as a core part of how we work.

If you care deeply about the craft of engineering and want to be part of a team that takes pride in what it builds, you'll feel right at home here.

About You

You are intellectually curious, analytically minded, and genuinely excited about the possibilities that AI and big data bring to business intelligence. You understand the fundamentals of machine learning and stay actively up to date with emerging AI models and developments in this fast-moving space.

You value diverse perspectives, communicate openly, and know how to thrive in a cross-functional team alongside product and engineering experts. You bring both rigor and creativity to your work and are energized by the challenge of turning complex data into meaningful insights.

We are looking for candidates who have:

• Proficiency in Python and PySpark, with demonstrated experience writing and reviewing production-quality analytical code.
• Working knowledge of AI models, including how they are deployed and how they differ fundamentally from training traditional machine learning models.
• Hands-on experience, or a strong willingness to learn, large-scale SaaS data analytics platforms, with an understanding of how data flows from ingestion through to insight delivery.

Nice to Have

• Experience with Agentic AI frameworks and designing autonomous workflow pipelines.
• Familiarity with MLOps practices, including model evaluation, monitoring, and lifecycle management.
• Experience building or working with evaluation and testing frameworks for AI/ML models.
• Knowledge of data lake architectures and working with both structured and unstructured data at scale.
• Experience with API design and development, particularly permissioned, queryable data APIs.
• A track record of continuous learning through certifications, side projects, open-source contributions, or active engagement with the AI/ML community.
• Strong communication and collaboration skills, with experience working in agile, cross-functional product and engineering teams.