Arlo
Data Engineer
New York City
Sponsorship not specified$180k-$220kDetected 29 days ago
PythonSQLAWSGCPAzureMachine LearningAirflowdbtData EngineeringData ScienceMLOpsCollaborationActuarial ScienceUnderwriting
About the role
- Arlo quotes small businesses using AI-powered underwriting, and the quality of that underwriting is only as good as the data beneath it.
- This is a hands-on individual contributor role.
- You'll sit at the boundary between data engineering and data science, working directly with underwriting, pricing, and analytics teams to ensure the right data reaches the right systems at the right time.
Responsibilities
- middlemen taking a cut, fraud nobody stops, and billing systems designed to fight over payment instead of deliver care.
- Maintain clear documentation of known data quality limitations so downstream teams know what the data can and cannot reliably support
- 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
- We're hiring a Data Engineer to build and maintain the pipelines, models, and monitoring systems that keep our data infrastructure clean, timely, and trustworthy.
- 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
- Build monitoring and alerting for data inconsistencies: duplicate records, mismatched member IDs, enrollment timing gaps, and carrier reporting lags
- We use it across underwriting, operations, clinical programs, and member experience to build an insurer that becomes more efficient as the technology improves.
Requirements
- 3-5 years in a data engineering or backend engineering role with significant data pipeline ownership
- Proficiency in Python and SQL
- 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
Nice to have
- Experience supporting ML feature pipelines or working alongside data science teams
- Familiarity with MLflow or similar MLOps tooling
- There's no separate ML engineering handoff
- you'll work directly with the people who depend on your pipelines daily.
- The role requires equal comfort in Python-based engineering and SQL-driven analysis, and a genuine interest in understanding the business context behind the data.
- Intro call with our recruiter
- Resume interview with an Arlo co-founder
- Technical take-home challenge (data engineering problem)
Skills
- Arlo is an equal opportunity employer.
- 🔒 Your safety matters to us.
Compensation
- $180,000 - $220,000 + equity
Benefits
- $180,000 - $220,000 + equity
- Apply AI to a problem that matters: Instead of optimizing ads or cutting labor costs, you'll use AI to fundamentally reimagine how people get healthcare.
- Instead of optimizing ads or cutting labor costs, you'll use AI to fundamentally reimagine how people get healthcare.
Company info
- You'll get real responsibility from day one-our high-trust team empowers you to run with big problems and shape core parts of the company.
Equal opportunity
- equal opportunity employer.
This listing is sourced directly from Arlo's careers page and normalized into a canonical job model.