Senior Data Engineer; AWS, Databricks

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

Salary Range: $ - $

Target Openings: 1

Employees are also eligible for performance-based cash incentive awards as part of a comprehensive compensation and benefits program.

The Travelers Data Engineering team constructs pipelines that contextualize and provide easy access to data by the entire enterprise. As a Senior Data Engineer you will accelerate growth and transformation of our analytics landscape. You will bring a strong desire to guide team members’ growth and develop data solutions that translate complex data into user-friendly terminology. You will leverage your ability to design, build and deploy data solutions that capture, explore, transform, and utilize data to support Artificial Intelligence, Machine Learning and business intelligence/insights.

• Design and build production data pipelines across AWS, Snowflake, Databricks supporting both batch and near real-time analytics workloads.
• Establish reusable engineering patterns and frameworks – parameterized, modular, idempotent pipeline templates that reduce duplicated effort and inconsistent implementations.
• Drive down lead time from commit to production by removing manual steps, leveraging AI, building self‑service tooling, and standardizing the path to deployment; treat cycle time as a metric you actively own and improve.
• Champion SDLC discipline covering version control, peer code review, automated testing, environment promotion, change management, and documentation.
• Integrate AI coding tools into daily workflow to accelerate scaffolding, refactoring, test generation, code optimization, and documentation with measurable impact on throughput and quality.
• Measure and demonstrate impact, tying AI‑tool adoption to concrete outcomes such as reduced lead time, faster test coverage, and improved consistency, and share those results to drive broader adoption.
• Evaluate emerging tooling and make pragmatic recommendations on what engineers should adopt, standardize on, or avoid.
• Data Ops:
  Blur the lines between data and software engineering practices. Employ CI/CD, automated testing, and apply trunk‑based or short‑lived branch development to data the same way it is to software.
• Modernize legacy workloads – help manage and optimize AbInitio pipelines and, when applicable, help migrate or re‑platform AbInitio pipelines toward cloud‑native, declarative, ELT‑based patterns on Snowflake and Databricks where it delivers value.
• Embed data quality, observability, and lineage into pipelines as a default – automated data tests, freshness/quality SLAs, and traceable lineage.
• Optimize for cost and performance across Snowflake compute, Databricks clusters, and storage, applying Fin Ops‑aware engineering practices.
• Mentor and upskill engineers through code review, pairing, design guidance, and documented standards, acting as a technical multiplier for the team.

• Bachelor’s Degree in STEM related field or equivalent.
• Ten years of related experience building, designing and operating production data pipelines at scale.
• Demonstrable experience architecting, designing and building scalable, secure data solutions using AWS, Databricks, Snowflake and AbInitio or similar platforms.
• A track record of leveraging AI assistants, creating skills/tools to augment data engineering practices throughout the development lifecycle.
• The ability to lead technical direction for data engineering…