✦ Luna Orbit — Data & Analytics

Lead Data Engineer, Imagery Models

at Travelers

📍 6 Locations Hybrid 💰 $161K – $266K USD / year Posted March 26, 2026
Salary $161K – $266K USD / year
Type Full-Time
Experience lead
Exp. Years 5+ years
Education Not specified
Category Data & Analytics

This role involves leading data engineering efforts to build and optimize data pipelines supporting AI and analytics initiatives. The candidate will oversee data solutions, collaborate with data scientists, and ensure scalable data architecture.

  • Lead data pipeline development
  • Collaborate with data science teams
  • Design scalable data architecture
  • Implement machine learning models
  • Oversee data integration and transformation

Technical scope includes designing data pipelines, implementing machine learning models, API integrations, and data transformation using Python and SQL in a hybrid environment.

The ideal candidate is a senior data engineer with over 5 years of experience designing data pipelines, implementing machine learning models, and leading data solutions in a hybrid work environment. They should have strong skills in Python and SQL and be capable of collaborating across teams to support analytics initiatives.

Data Pipeline DesignMachine LearningData EngineeringPythonSQL
Data ModelingAPI IntegrationData Transformation
PythonSQLAPIs
Data Pipeline DesignMachine LearningData EngineeringPythonSQL
Data Pipeline DesignMachine LearningData EngineeringPythonSQLData ModelingAPI IntegrationData Transformation
leadershipcollaborationstrategic thinkingcommunication
Industry Insurance
Job Function Leading data engineering projects to support enterprise analytics and AI models
Role Subtype Data Engineer
Tech Domains Python, SQL
Clearance Required None
Visa Sponsorship No
Data EngineerData PipelinesMachine LearningData EngineeringPythonSQLData ModelingAPIData Transformationleadhybridmultiple locationsdata solutionsanalyticsdata architecture

Less than 5 years of experience, No experience with data pipelines or machine learning, Lack of leadership experience, Unfamiliar with hybrid work environment

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