Data Engineer II (AWS, Python, SQL

Who Are We?

Taking care of our customers, our communities and each other. That’s the Travelers Promise. By honoring this commitment, we have maintained our reputation as one of the best property casualty insurers in the industry for over 170 years. Join us to discover a culture that is rooted in innovation and thrives on collaboration. Imagine loving what you do and where you do it.

The annual base salary range provided for this position is a nationwide market range and represents a broad range of salaries for this role across the country. The actual salary for this position will be determined by a number of factors, including the scope, complexity and location of the role; the skills, education, training, credentials and experience of the candidate; and other conditions of employment.

As part of our comprehensive compensation and benefits program, employees are also eligible for performance-based cash incentive awards.

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As a Data Engineer II at Travelers, you’ll play a meaningful role in how Business Insurance Property and General Liability pricing decisions are powered by data. Working alongside Actuarial and Data Science partners, you’ll help turn analytical ideas into trusted, production‑grade assets that directly influence pricing outcomes across core insurance products. You’ll build deep domain expertise in Property and GL while contributing to modernization efforts and future‑facing pricing capabilities.

Through complex, high‑visibility initiatives, your work will strengthen data reliability, model readiness, and long‑term scalability across pricing platforms. The ideal candidate is motivated by solving complex data engineering problems and collaborating across functions. Your contributions will help advance Travelers’ pricing strategies in a dynamic and evolving insurance marketplace.

• Own and deliver data pipeline development and ongoing support for Property and GL pricing use cases
• Lead model implementation for Property and GL pricing models in partnership with Data Science and Actuarial teams
• Build and serve as the GL data subject matter expert to support benchmarks, modernization initiatives, and future enhancements
• Provide project leadership, planning, and execution across Property and GL data engineering initiatives
• Execute complex data preparation activities, including exploration, cleansing, and transformation, with awareness of enterprise architecture, platforms, and downstream consumption patterns
• Translate actuarial and data science requirements into scalable, production‑ready data solutions
• Adopt and embed MLOps practices across the model development and implementation lifecycle
• Establish data quality controls, profiling, and monitoring for model implementation and ongoing performance
• Present analysis and technical recommendations to influence data architecture and implementation decisions
• Provide technical leadership through code reviews, design guidance, and mentoring within the team
• Apply agile methodologies to plan, prioritize, and deliver work across concurrent initiatives
• Perform other duties as assigned

• Bachelor’s Degree in STEM related field or equivalent.
• Eight years of related experience.
• Deep, hands‑on experience with modern engineering tools and practices, including:
  • Cloud platforms (preferably AWS) and model implementation in the cloud
  • Programming in Python and SQL
  • Working with data engineering concepts and building/maintaining data pipelines
  • AI‑native solutions and modern software engineering practices (e.g., APIs, microservices, test automation)
• Proven ability to deliver high‑quality solutions at a steady, predictable pace:
  • Breaks work into small, releasable increments
  • Delivers complete, robust solutions while effectively managing tradeoffs
• Demonstrated domain expertise, including:
  • Strong understanding of relevant technical concepts and industry trends
  • In‑depth knowledge of the systems you’ve worked on and familiarity with adjacent systems
• Strong problem‑solving skills with a focus on building resilient, long‑lived systems and finding innovative ways to…