The Opportunity

Arlo quotes small businesses using AI-powered underwriting, and the quality of that underwriting is only as good as the data beneath it. 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.

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.

What You'll Work On

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 We're Looking For

Required

• 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

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

How You'll Work

You'll own your projects end-to-end - from initial scoping through to production deployment and ongoing monitoring. 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.

Interview Process

1. Intro call with our recruiter
2. Resume interview with an Arlo co-founder
3. Technical take-home challenge (data engineering problem)
4. Onsite (or virtual): technical review + behavioral/cultural interviews

Compensation

$180,000 - $220,000 + equity

Why Join Arlo:

• High ownership: 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.
• Join an important mission: Your work directly influences how people access care and improves lives at scale.
• Growth & expansion: We're moving fast, and as we grow, your scope will grow with us-new challenges, bigger opportunities, and rapid career velocity.
• 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.
• High pace, high collaboration: We operate with velocity, first-principles thinking, and a team that works closely, openly, and with ambition.

Exact compensation inclusive of salary and any bonuses is determined based on a number of factors including experience and skill level, location, and qualifications which are assessed during the interview process.