Data Engineer
Confirmed on the employer's own hiring board on Oct 11, 2026. First seen by Alion on Oct 5, 2026.
Are you passionate about transforming complex insurance data into valuable insights? Join our Underwriting Data and Analytics team where you'll leverage cutting-edge technologies to build scalable data solutions that drive strategic underwriting decisions and business growth. This is your opportunity to apply your extensive PySpark and TypeScript expertise in an environment that values innovation and technical excellence.
About the Role
As a Data Engineer, you'll be at the forefront of our data transformation journey, architecting and implementing robust data pipelines that power critical underwriting analytics. You'll collaborate with cross-functional teams to deliver high-quality data products while maintaining the highest standards of data governance and engineering excellence.
Key Responsibilities
- Design and build robust, scalable data pipelines (batch and streaming) that transform complex insurance data into actionable insights
- Engineer and maintain analytics-ready data products with clear contracts and comprehensive documentation
- Ensure high data quality, observability, lineage, and reliability across all data pipelines
- Own end-to-end delivery of data engineering initiatives from design through production implementation
- Break down complex requirements into executable technical plans and deliver against aggressive timelines
- Proactively identify and resolve bottlenecks, technical debt, and operational risks before they impact business operations
- Establish and enforce engineering standards including coding practices, testing protocols, CI/CD pipelines, and data modeling approaches
- Drive reusability and simplification across the data ecosystem to reduce fragmentation and technical debt
- Collaborate with analytics, data science, underwriting, and business teams to translate needs into scalable solutions
- Communicate effectively on trade-offs, timelines, and risks to technical and non-technical stakeholders
- Provide technical guidance and constructive code/design reviews to help elevate team capabilities
About the Team
The Underwriting Data and Analytics team is responsible for transforming complex insurance data into actionable insights that power strategic underwriting decisions. We design and maintain scalable data platforms that enable advanced analytics, machine learning applications, and datadriven decision making across the underwriting lifecycle. Our team combines deep insurance domain knowledge with cutting-edge data engineering expertise to deliver solutions that drive business value.
About You
You're a seasoned data engineer with a proven track record of building enterprise-grade data solutions in complex environments. You thrive in collaborative settings where you can apply both your technical expertise and business acumen to solve challenging problems. You're passionate about data quality, engineering excellence, and continuous improvement, with a keen eye for balancing immediate delivery needs with long-term architectural sustainability.
We are looking for candidates who meet these requirements:
- Bachelor's or Master's degree in Computer Science, Engineering, or related technical field
- 8+ years of professional experience in data engineering, with at least 5 years specifically in the insurance or financial services industry
- Advanced expertise in PySpark for large-scale data processing, including performance optimization and best practices
- Strong proficiency in TypeScript for developing robust, type-safe applications and data services
- Experience with Palantir platforms and tools for data integration and analytics workflows
- Extensive experience designing, implementing, and maintaining complex ETL/ELT pipelines in cloud environments (preferably AWS or Azure)
- Proven track record of implementing data governance, quality, and lineage solutions at enterprise scale
- Deep knowledge of data modeling techniques and best practices for both analytical and operational data store.
These are additional nice to haves:
- Experience with real-time data streaming technologies (Kafka, Kinesis, etc.)
- Familiarity with containerization and orchestration tools (Docker, Kubernetes)
- Knowledge of modern data lakehouse architectures (Delta Lake, Iceberg, etc.)
- Experience with DataOps and MLOps practices and tools
- Understanding of insurance underwriting processes and data requirements
- Certifications in relevant cloud platforms or data technologies
- Experience mentoring junior engineers and contributing to community knowledge sharing
- Familiarity with data visualization tools and techniques
Our company has a hybrid work model where the expectation is that you will be in the office at least three days per week
About Swiss Re
Swiss Re is one of the world’s leading providers of reinsurance, insurance and other forms of insurance-based risk transfer, working to make the world more resilient. We anticipate and manage a wide variety of risks, from natural catastrophes and climate change to cybercrime. We cover both Property & Casualty and Life & Health. Combining experience with creative thinking and cutting-edge expertise, we create new opportunities and solutions for our clients. This is possible thanks to the collaboration of more than 15,000 employees across the world.
Our success depends on our ability to build an inclusive culture encouraging fresh perspectives and innovative thinking. We embrace a workplace where everyone has equal opportunities to thrive and develop professionally regardless of their age, gender, race, ethnicity, gender identity and/or expression, sexual orientation, physical or mental ability, skillset, thought or other characteristics. In our inclusive and flexible environment everyone can bring their authentic selves to work and their passion for sustainability.
If you are an experienced professional returning to the workforce after a career break, we encourage you to apply for open positions that match your skills and experience.
We may use AI-powered tools to support the review and evaluation of applications for this position. These tools provide additional insights to our recruitment teams, but all hiring decisions are carefully reviewed and made by people. To learn more about how we use AI in recruitment and how we handle your personal data, please review our Data Privacy Statement before applying.
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