Data Engineer II & MLOps Engineer — Shape Production ML

Travelers; Hartford (CT); On-site; USD 127,000 - 209,000; Full time

Job summary

Travelers in Hartford, CT is seeking a Data Engineer II to turn ML experiments into production-grade data workflows. You will work in Databricks daily, implementing scalable pipelines and partnering with data scientists and engineers to ship reliable data products.

You will apply DevOps practices, ensure data governance and quality, and build reusable components that help teams understand what is running, where, and how it performs.

Qualifications

• Bachelor's degree in STEM field or equivalent.
• 8+ years of related data/ML engineering experience.
• Experience building and deploying ML pipelines in a modern MLOps environment.
• Strong Python, SQL, and Databricks skills.
• Experience with CI/CD, monitoring and reliability in data workflows.

Responsibilities

• Build and operationalize complex data solutions, correct problems, apply transformations, and recommend 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.

Skills

ML pipelines

Python

SQL

Databricks

CI/CD

Cloud platforms

Education

Bachelor's degree in STEM related field

8 years of related experience

Tools

EKS / Kubernetes

Cloud platforms

Job description

Travelers in Hartford, CT is seeking a Data Engineer II to turn ML experiments into production-grade data workflows. You will work in Databricks daily, implementing scalable pipelines and partnering with data scientists and engineers to ship reliable data products.

You will apply DevOps practices, ensure data governance and quality, and build reusable components that help teams understand what is running, where, and how it performs.