Data Engineer II - MLOps Engineer

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.

Data Analytics, Data Science, Technology

Compensation Overview

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.

Salary Range

$126,500.00 - $208,700.00

1

As a Data Engineer II on this team, you turn ML from a promising notebook into a reliable product. You work in Databricks every day, shaping real workloads: taking messy experiments and turning them into clean, repeatable jobs and workflows, and wiring MLflow so every run is traceable. You own the path that makes models visible and operable from day one by instrumenting pipelines with observability events and standing up the APIs and scripts that keep the model inventory and governance picture complete. You sit with data scientists and engineers in the same repo, pair on jobs that matter to the business, and turn the patterns you discover into lightweight libraries, templates, and Backstage views that other teams adopt because they save time. If you like shipping code that many teams depend on, making complex systems feel simple, and being the person who can tell anyone “what is running, where, and how it is doing,” this role gives you that kind of impact.

What Will You Do?

• Build and operationalize complex data solutions, correct problems, apply transformations, and recommending data cleansing/quality solutions.
• Design complex data solutions
• Perform analysis of complex sources to determine value and use and recommend data to include in analytical processes.
• Incorporate core data management competencies including data governance, data security and data quality.
• Collaborate within and across teams to support delivery and educate end users on complex data products/analytic environment.
• Perform data and system analysis, assessment and resolution for complex defects and incidents and correct as appropriate.
• Test data movement, transformation code, and data components.
• Perform other duties as assigned.

What Will Our Ideal Candidate Have?

• Bachelor’s Degree in STEM related field or equivalent.
• Eight years of related experience.
• Build, deploy, and support ML pipelines in a modern MLOps environment, partnering closely with data scientists using classic ML techniques (GBMs, linear models) and GenAI-based solutions.
• Apply strong software engineering and DevOps practices (CI/CD, monitoring, reliability) to data and ML workflows, working comfortably across both data engineering and application engineering domains
• Develop robust data solutions using Python, SQL, and Databricks; experience with EKS (or other Kubernetes-based platforms) for scalable data/ML workloads is a strong plus.
• Leverage experience in financial services or other regulated industries to quickly understand business context and deliver production-grade ML and data solutions with minimal hand-holding.
• Collaborate with teams adopting GenAI tools (e.g., Claude) and traditional ML, acting as a power user and enabler rather than a pure researcher, with a focus on stability, performance, and operational excellence.
• Highly proficient use of tools, techniques, and manipulation including Cloud platforms, programming languages, and a full understanding of modern software engineering practices.
• The ability to deliver work at a steady, predictable pace to achieve commitments, deliver complete solutions but release them in small batches, and identify and negotiate important tradeoffs.
• Demonstrated track record of domain expertise including understanding technical concepts necessary and industry trends, and possess in-depth knowledge of immediate systems worked on and some knowledge of adjacent systems.
• Strong problem solver who ensures systems are built with longevity and creates innovate ways to resolve issues.
• Strong written and verbal communication skills with the ability to work collaborate well with team members and business partners.
• Ability to lead team members and help create a safe environment for others to learn and grow as engineers. and a proven track record of self-motivation in identifying opportunities and tracking team efforts.

What is a Must Have?

• Bachelor’s degree in computer science, related STEM field, or its equivalent in education and/or work experience.
• 4 additional years of data engineering experience.