Software Engineer - Data

Authentic•New York, NY

79d

About The Position

Authentic helps insurance brokers create better insurance products for their clients. We provide the technology platform and behind-the-scenes support that lets brokers build custom insurance solutions for specific industries or customer groups. Think of us as the operating system that powers these specialized insurance offerings. For brokers, this means they can offer unique products their competitors don't have, earn more money, and spend less time on paperwork and administrative tasks. We handle the complex (technology, compliance, and partnerships with highly-rated insurance carriers), so they can focus on serving their customers and growing their business.

Requirements

3-5 years of experience building and maintaining data pipelines and infrastructure in production environments
Strong proficiency with modern data stack tools: Snowflake, dbt, and Airflow (or similar orchestration tools)
Experience with Python for data processing and pipeline development
Solid understanding of data modeling best practices (dimensional modeling, slowly changing dimensions, etc.)
Familiarity with AWS data services and infrastructure-as-code
Demonstrated ownership mindset: you clarify requirements, ask great questions, and drive work to completion
Track record of building reliable systems without sacrificing velocity—you know when to move fast and when to be careful

Responsibilities

Design and build reliable data pipelines and infrastructure that power underwriting models, claims processing, and operational reporting
Own data models end-to-end—from source ingestion through transformation to delivery—ensuring data quality, freshness, and accessibility for internal teams, product features, and external partners
Identify bottlenecks in existing data workflows and implement solutions that improve reliability, reduce latency, and scale with business growth
Build and maintain the data warehouse architecture, optimizing for both analytical workloads and operational use cases
Partner with engineering, product, and ops teams to understand data needs and translate them into well-designed pipelines and models
Implement monitoring, alerting, and data quality checks to catch issues before they impact downstream consumers
Contribute to data governance practices, including documentation, lineage tracking, and access controls for regulatory and compliance needs