Zurich Insurance
Senior Data Engineer
Senior Data Engineer at Zurich Insurance designing, building and operating scalable data and AI solutions for Retail Claims, Pricing and Underwriting. Day-to-day: ETL/ELT pipelines with Spark and Databricks on AWS/Azure, CI/CD, cloud microservices, MLOps, and LLM-powered products, primarily in Python.
- Compensation
- Not disclosed
- Location
- Barcelona, Spain
- Work setup
- Hybrid
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 2+ years
- Category
- Data engineering
- Posted
Skills
Must have
- Advanced knowledge of Python, Databricks, AWS, Docker, Kubernetes or similar, Git, LLMs, Spark, and Azure DevOps or Jenkins
- 5-10 years of experience building production-grade data and backend solutions with Python
- 5 years of cloud engineering experience with AWS or Azure, preferably in data-intensive environments
- 5-10 years of experience working with Databricks as a cloud-based data and AI platform
- 5 years of experience designing multi-cloud CI/CD pipelines and automated deployments for infrastructure, data and ML
- 5 years of experience designing, implementing and operating distributed services and APIs
- 2 years of experience integrating OpenAI, Anthropic, AI agents or MCP-based services into real-world applications
- 2 years of experience building and operating APIs and end-to-end backend services
- English language proficiency (level 3/5)
- Candidates must already be eligible to work in the EU
Nice to have
- Experience in insurance, financial services or another highly regulated industry
- Exposure to Claims, Pricing or Underwriting domains and their data
Full description
We're looking for a Senior Data Engineer to design, build, and operate scalable data and AI solutions for Retail Claims, Pricing, and Underwriting.
🎯 Responsibilities
- Design and build end-to-end data and AI solutions, including scalable, secure, and observable data pipelines and services from ingestion through production.
- Apply modern software development lifecycle practices across data and AI engineering projects.
- Implement high-performance ETL/ELT processes in AWS or Azure using Spark and Databricks for batch and real-time workloads.
- Ensure strong data quality, lineage, reliability, and operational performance.
- Build and maintain CI/CD pipelines using tools such as Azure DevOps and Jenkins for data platforms and machine learning products.
- Automate testing, deployment, and monitoring across environments.
- Design, implement, and operate cloud-native microservices and APIs that expose data and AI capabilities.
- Deploy services in secure and resilient cloud environments.
- Define and implement MLOps strategies, including versioning, feature stores, monitoring, drift detection, and model lifecycle management.
- Collaborate with Data Science and Architecture teams to productionise machine learning models.
- Translate Claims, Pricing, and Underwriting requirements into robust technical architectures.
- Influence design decisions and ensure solutions are both technically feasible and valuable to the business.
- Design and implement AI-powered products using large language models, including prompt engineering, fine-tuning, retrieval-augmented generation (RAG), and agentic workflows.
- Collaborate with actuaries, data scientists, product owners, and business subject-matter experts to deliver production-ready solutions.
🛠️ Requirements
- Advanced knowledge of Python, Databricks, AWS, Docker, Kubernetes or similar technologies, Git or similar tools, LLMs, Spark, and Azure DevOps or Jenkins.
- 5–10 years of experience building production-grade data and backend solutions with Python.
- 5 years of cloud engineering experience with AWS or Azure, preferably in data-intensive environments.
- 5–10 years of experience working with Databricks as a cloud-based data and AI platform.
- 5 years of experience designing multi-cloud CI/CD pipelines and automated deployments for infrastructure, data, and machine learning.