Data Engineer

The Opportunity

Aro is rebuilding health insurance for small businesses from first principles: making sure as much of every premium dollar as possible goes to care instead of getting absorbed by the system around it. We do that by identifying fraud earlier, steering members toward higher-quality and lower-cost care, automating operational overhead, and eliminating vendors whose business exists mostly to take a cut. AI is the foundation that makes this work. We use it across underwriting, operations, clinical programs, and member experience to build an insurer that becomes more efficient as the technology improves.

We're already operating at meaningful scale: profitable, hundreds of millions in premiums, tens of thousands of members covered, and growing quickly through brokers, employers, and partners. Backed by Upfront Ventures, 8VC, and General Catalyst, with a team from Palantir, YC companies, and longtime healthcare operators.

Pipeline Development and Maintenance

• Build and maintain ingestion pipelines for complex, heterogeneous data sources — TPA feeds, carrier data, census files, claims, eligibility, and enrollment records
• Design and implement dbt models and transformation logic that produce clean, reliable "source of truth" tables used across underwriting, pricing, and reporting
• Own pipeline orchestration using tools like Dagster or Airflow, ensuring reliable scheduling, retries, and alerting

Data Quality and Observability

• Build monitoring and alerting for data inconsistencies: duplicate records, mismatched member IDs, enrollment timing gaps, and carrier reporting lags
• Profile ingest delay characteristics across live policy data and flag where structural latency introduces systematic bias
• Maintain clear documentation of known data quality limitations so downstream teams know what the data can and cannot reliably support

Collaboration with Data Science

• Partner closely with the data science team to build and maintain feature pipelines that feed underwriting and pricing models
• Support feedback loop infrastructure that carries post-quoting learnings back into upstream models
• Work with engineering to prioritize data quality fixes and accelerate resolution of upstream issues

What You'll Work On

• Develop and maintain ingestion pipelines for complex, heterogeneous data sources
• Design and implement dbt models and transformation logic
• Own pipeline orchestration using tools like Dagster or Airflow
• Build monitoring and alerting for data inconsistencies
• Profile ingest delay characteristics across live policy data
• Document known data quality limitations
• Collaborate with data science team to build and maintain feature pipelines
• Support feedback loop infrastructure
• Work with engineering to prioritize data quality fixes

Requirements

• 3–5 years in a data engineering or backend engineering role with significant data pipeline ownership
• Proficiency in Python and SQL; comfortable writing production-quality code in both
• Hands-on experience with pipeline orchestration tools (Dagster, Airflow, Prefect, or similar)
• Experience with dbt or equivalent transformation frameworks
• Familiarity with cloud data environments (AWS, GCP, or Azure) and columnar/analytical databases
• Track record working with messy, real-world datasets and building systems that handle inconsistency gracefully
• Strong instincts around data quality — you catch problems before they reach downstream consumers

Qualifications

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