Sr. Software Engineer - Data

Authentic•New York, NY

86d

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

5+ years of experience building and maintaining data pipelines and infrastructure in production environments at scale
Strong proficiency with modern data stack tools: Snowflake, dbt, and Airflow (or similar orchestration tools)
Deep experience with Python for data processing and pipeline development
Expert understanding of data modeling best practices (dimensional modeling, slowly changing dimensions, data vault, etc.)
Familiarity with AWS data services and infrastructure-as-code
Demonstrated high agency and ownership: you naturally clarify what "good" looks like, ask great questions, and drive to completion
Track record of building reliable systems without sacrificing velocity—you know when to move fast and when to be careful
Experience mentoring engineers and elevating team capabilities

Responsibilities

Lead the design and implementation of data pipelines and infrastructure that power underwriting models, claims processing, and operational reporting—taking ownership from problem definition through delivery
Own the data warehouse architecture, making decisions that balance analytical performance, operational needs, and long-term scalability
Set technical standards and best practices for data modeling, pipeline development, and data quality across the team
Identify bottlenecks across the data stack and build solutions that improve reliability, reduce latency, and scale with business growth
Partner with engineering, product, and ops teams to understand data needs and translate them into well-designed systems
Build monitoring, alerting, and data quality frameworks that catch issues before they impact downstream consumers
Drive data governance practices, including documentation, lineage tracking, and access controls for regulatory and compliance needs
Mentor and coach other engineers on the team, helping them grow technically and develop ownership mindsets