Data Engineer II (Tech Lead) - AWS, Databricks, Snowflake

Travelers•Hartford, CT

About The Position

Travelers Data Engineering team constructs pipelines that contextualize and provide easy access to data by the entire enterprise. As a Data Engineer, you will play a key role in growing and transforming our analytics landscape. In addition to your strong analytical mind, you will bring your inquisitive attitude and ability to translate stories found in data by leveraging a variety of data programming techniques. 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.

Requirements

Bachelor’s Degree in STEM related field or equivalent.
Eight years of related experience.
Demonstrable experience architecting, designing and building scalable, secure data solutions using AWS, Databricks, Snowflake and Ab>Initio or similar platforms.
A track record of leveraging AI assistants, create skills/tools to augment data engineering practices throughout the development lifecycle.
The ability to lead technical direction for data engineering initiatives across cloud and on-premises infrastructure.
Willingness to run mentoring sessions and offer technical guidance to the 20-person admin team.
Ability to manage infrastructure deployment and optimize cloud resources.
Drive to learn, identify and set technical standards and influence engineering practices and data governance policies.
Ability to lead and take action even when there is no clear owner, inspire and motivate others, and be effective at influencing team members.
Cloud: Proficiency with commonly used AWS services and architectures for data and analytics solutions
Databricks: Workspace management, cluster configuration, open table formats (Iceberg, Delta Lake), Unity Catalog, building and tuning Spark/pySpark/SQL workloads.
Data Warehousing: Strong understanding of data modeling, dimensional modeling (star/snowflake schemas) and medallion (bronze/silver/gold) architecture.
AI Coding Assistants: Familiarity using Claude Code, Codex, Copilot, Cursor, etc. for day-to-day engineering tasks.
AbInitio: Proficiency with GDE, Co>Operating System, and EME, including maintaining and optimizing existing graphs.
CI/CD for data: Git-based workflows, branching strategy, and pipeline automation (e.g., GitHub Actions, GitLab CI, or Jenkins).
Infrastructure as Code (e.g., Terraform): Familiarity with a configuration-driven, repeatable approach to environments
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.
a proven track record of self-motivation in identifying opportunities and tracking team efforts.
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.

Responsibilities

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.