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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer - **Company:** Arlo Technologies, Inc. - **Location:** New York, NY, United States - **Experience:** Experienced - **Salary:** $180,000.0 - $220,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Data Analysis, Microsoft Azure, Cloud Database, Databases, Information Engineering, Data Infrastructure, Monitoring of Systems, Intrusion Detection Systems, Python (Programming Language), Machine Learning, SQL Databases, Data Ingestion, Production Code, Health Level Seven International, Machine Learning Operations, Data Inconsistencies, Data Pipelines - **Published:** June 24, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=7471b510bbb8a2e5 ## About the Role Do you have experience in Tooling?, 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 ## Description 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, 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. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Kubernetes and Microservices with Multi-Model Databases](https://www.wearedevelopers.com/videos/382-kubernetes-and-microservices-with-multi-model-databases) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning) - [Data Science on Software Data](https://www.wearedevelopers.com/videos/162-data-science-on-software-data) ## Related Articles - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Dev Digest 162: AI careers, MCP, AWS best practices & floppy sweaters](https://www.wearedevelopers.com/magazine/571-dev-digest-162-ai-careers-mcp-aws-best-practices-floppy-sweaters) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers)