Data Engineer

About the Company

Arlo is rebuilding health insurance for small businesses from first principles. We use AI to identify fraud, steer members toward high-quality care, and automate operational overhead to ensure more premium dollars go toward actual medical care.

Responsibilities

• Build and maintain ingestion pipelines for complex data sources including TPA feeds, carrier data, and claims
• Design and implement dbt models and transformation logic to produce reliable source-of-truth tables
• Own pipeline orchestration using tools like Dagster or Airflow
• Build monitoring and alerting for data inconsistencies and quality observability
• Partner with data science teams to build and maintain feature pipelines for underwriting and pricing models
• Support feedback loop infrastructure to carry post-quoting learnings back into upstream models

Requirements

• 3–5 years in a data engineering or backend engineering role with pipeline ownership
• Proficiency in Python and SQL
• Hands-on experience with pipeline orchestration tools (Dagster, Airflow, or Prefect)
• Experience with dbt or equivalent transformation frameworks
• Familiarity with cloud data environments (AWS, GCP, or Azure) and analytical databases
• Proven track record of handling messy, real-world datasets

Preferred Qualifications

• Background in health insurance, claims data, or actuarial/TPA environments
• Experience supporting ML feature pipelines or working with data science teams
• Familiarity with MLflow or MLOps tooling
• Exposure to healthcare data standards or regulated data environments