✦ Luna Orbit — Data & Analytics

Analytics Engineer Summer- Woodland Hills, CA

at Farmers Insurance Exchange

📍 Woodland Hills, CA, US, 91367 Hybrid 💰 $17 – $28 USD / year Posted March 14, 2026
Salary $17 – $28 USD / year
Type Internship
Experience intern
Exp. Years 0-1 years
Education Quantitative academic fields
Category Data & Analytics

Farmers Insurance offers a summer internship for aspiring data engineers to work on transforming raw data into high-quality datasets, gaining practical experience in data analysis and pipelines.

  • Transform raw data
  • Analyze datasets
  • Build data pipelines
  • Ensure data quality
  • Support data modeling

Internship role involves data transformation, analysis, modeling, and pipeline development using SQL and Python in a hybrid environment.

The ideal candidate is a motivated student or recent graduate with a quantitative background, interested in data transformation, analysis, and pipelines. They should be eager to learn data engineering concepts and contribute to real-world projects.

Quantitative academic backgroundInterest in data transformation and analysisWillingness to learn and contribute
Experience with data engineeringKnowledge of data qualityExperience with data modelingExperience with data pipelinesData visualization skills
SQLPythonData pipelinesData modelingData visualization
Data transformationData analysisData qualitySQLPythonData engineeringData setsData modelingData pipelinesData visualization
Data transformationData analysisData qualitySQLPythonData engineeringData setsData modelingData pipelinesData visualization
CuriosityPassionHard-workingCollaborationProblem-solving
Industry Insurance / Financial Services
Job Function Assist in data transformation, analysis, and pipeline development for insurance data
Role Subtype Data Engineer
Tech Domains SQL, Python
Data transformationData analysisData qualitySQLPythonData engineeringData setsData modelingData pipelinesData visualizationInternshipData skillsQuantitative backgroundData projects

Lack of quantitative academic background, No interest in data transformation or analysis, No willingness to learn or contribute, Lack of basic data skills

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