Data Engineer II & MLOps Engineer: Production ML Pipelines

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

Benefits offered by this job

Health Insurance

Retirement 401(k) match

Paid Time Off

Wellness Program

Volunteer Encouragement

Job summary

Travelers is seeking a Data Engineer II in Hartford, CT, to turn ML experiments into reliable production, using Databricks and MLflow. You will partner with data scientists and engineers, instrument pipelines for observability, and build APIs and templates used across teams to govern model inventories.

You will design and optimize data pipelines, ensure governance and security, and promote reliability and scalable data solutions in a fast-paced, collaborative environment.

Qualifications

• Bachelor’s degree in STEM or equivalent education/experience.
• Eight years of related data engineering experience.
• Build, deploy, and support ML pipelines in a modern MLOps environment.
• Strong software engineering and DevOps practices applied to data workflows.

Responsibilities

• Build and operationalize complex data solutions and data cleansing.
• Design complex data solutions and assess data sources for value.
• Incorporate data governance, security and quality.
• Collaborate across teams to support delivery and educate end users.
• Analyze defects and incidents; resolve root causes.
• Test data movement and transformation code.
• Perform other duties as assigned.

Skills

Python

SQL

Databricks

ML pipelines

CI/CD

Kubernetes

DevOps practices

Data governance

Communication

Education

Bachelor's degree in STEM

Tools

MLflow

EKS / Kubernetes

Backstage

Databricks

Job description

Travelers is seeking a Data Engineer II in Hartford, CT, to turn ML experiments into reliable production, using Databricks and MLflow. You will partner with data scientists and engineers, instrument pipelines for observability, and build APIs and templates used across teams to govern model inventories.

You will design and optimize data pipelines, ensure governance and security, and promote reliability and scalable data solutions in a fast-paced, collaborative environment.