Data Engineer

Arlo • New York, NY

17d • $180,000 - $220,000

About The Position

Arlo is rebuilding health insurance for small businesses from first principles, focusing on ensuring that a larger portion of premium dollars goes towards care rather than administrative overhead. They achieve this by identifying fraud, guiding members to better and more affordable care, automating operational tasks, and eliminating unnecessary vendors. Artificial intelligence is central to their approach, being used across underwriting, operations, clinical programs, and member experience to create an increasingly efficient insurer.

The company is already operating at a significant scale, being profitable with hundreds of millions in premiums and tens of thousands of members covered. They are growing rapidly through brokers, employers, and partners, and are backed by prominent venture capital firms. The team comprises individuals from companies like Palantir and YC startups, as well as experienced healthcare professionals.

This role is crucial for Arlo's AI-powered underwriting process, as the quality of underwriting depends on the underlying data. The Data Engineer will be responsible for building and maintaining the data infrastructure, including pipelines, models, and monitoring systems, to ensure the data is clean, up-to-date, and reliable.

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; ability to identify and address problems before they impact downstream consumers.

Nice To Haves

• 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.

Responsibilities

• Build and maintain ingestion pipelines for complex, heterogeneous data sources such as TPA feeds, carrier data, census files, claims, eligibility, and enrollment records.
• Design and implement dbt models and transformation logic to create clean, reliable 'source of truth' tables for underwriting, pricing, and reporting.
• Manage pipeline orchestration using tools like Dagster or Airflow, ensuring reliable scheduling, retries, and alerting.
• Develop monitoring and alerting systems for data inconsistencies, including duplicate records, mismatched member IDs, enrollment timing gaps, and carrier reporting lags.
• Analyze and profile data ingestion delay characteristics to identify where structural latency might introduce systematic bias.
• Maintain comprehensive documentation of data quality limitations for downstream teams.
• Collaborate with the data science team to build and maintain feature pipelines for underwriting and pricing models.
• Support the infrastructure for feedback loops that transfer post-quoting learnings back into upstream models.
• Work with the engineering team to prioritize data quality fixes and expedite the resolution of upstream issues.
• Own projects end-to-end, from initial scoping and production deployment to ongoing monitoring.

Benefits

• Equity
• High ownership: Real responsibility from day one, empowering individuals to run with big problems and shape core parts of the company.
• Important mission: Work directly influences how people access care and improves lives at scale.
• Growth & expansion: Opportunities for scope to grow with the company, leading to new challenges, bigger opportunities, and rapid career velocity.
• Apply AI to a problem that matters: Use AI to fundamentally reimagine healthcare, rather than optimizing ads or cutting labor costs.
• High pace, high collaboration: Work in an environment with velocity, first-principles thinking, and a closely collaborating team.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed