Senior Data Engineer

About COVU
COVU is a venture-backed technology startup transforming the insurance industry. We empower independent agencies with AI-driven insights and digitized operations, enabling them to manage risk more effectively. Our team is building an AI-first company set to redefine the future of insurance distribution.

Location:

This role can be hybrid or remote. If the candidate is based in the Los Angeles (LA) area, it will be a hybrid role working from our office in West Hollywood. For candidates based anywhere else in the US, this will be a fully remote role.

Role Overview

 We are seeking an experienced and product-focused Senior Data Engineer to be a core member of our Platform product team. This is a high-impact role where you will play a pivotal part in evolving our core data infrastructure.

Your primary mission will be to develop key components of our "Policy Journal" - the foundational data asset that will serve as the single source of truth for all policy, commission, and client accounting information. You will work closely with the Lead Data Engineer and business stakeholders to translate requirements into robust data models and scalable pipelines that drive analytics and operational efficiency for our agents, managers, and leadership.

This role requires a blend of greenfield development, strategic refactoring of existing systems, and a deep understanding of how to create trusted, high-quality data products.

What You’ll Do:

• Develop the Policy Journal: Be a primary builder of our master data solution that unifies policy, commission, and accounting data from sources like IVANS and Applied EPIC. You will implement the data models and pipelines that create the "gold record" powering our platform.
• Ensure Data Quality and Reliability: Implement robust data quality checks, monitoring, and alerting to ensure the accuracy and timeliness of all data pipelines. You will champion and contribute to best practices in data governance and engineering.
• Build the Foundational Analytics Platform: Implement and enhance our new analytics framework using modern tooling (e.g., Snowflake, dbt, Airflow). You will build and optimize critical data pipelines, transforming raw data into clean, reliable, and performant dimensional models for business intelligence.
• Modernize Core ETL Processes: Systematically refactor our existing Java & SQL (PostgreSQL) based ETL system. You will identify and resolve core issues (e.g., data duplication, performance bottlenecks), strategically rewriting critical components in Python and migrating orchestration to Airflow.
• Implement Data Quality Frameworks: Working within our company's QA strategy, you will build and execute automated data validation frameworks. You will be responsible for writing tests that ensure the accuracy, completeness, and integrity of our data pipelines and the Policy Journal.
• Collaborate and Contribute to Design: Partner with product managers, the Lead Data Engineer, and business stakeholders to understand complex business requirements. You will be a key technical contributor, translating business needs into well-designed and maintainable solutions.
  

What We're Looking For:

• 5+ years of experience in data engineering, with a proven track record of building and maintaining scalable data pipelines in production.
• Expert-level proficiency in Python and SQL.
• Deep Airflow & DAG Expertise: Proven hands-on experience designing, creating, and independently managing multiple complex DAGs (Directed Acyclic Graphs) within Airflow to robustly orchestrate interdependent enterprise-scale data pipelines.
• Strong experience with modern data stack technologies, including a cloud data warehouse (Snowflake or Redshift), a workflow orchestrator (Airflow is highly preferred), and data transformation tools.
• Hands-on experience with AWS data services (e.g., S3, Glue, Lambda, RDS).
• Experience in the insurance technology (insurtech) industry and familiarity with insurance data concepts (e.g., policies, commissions, claims).
• Demonstrated ability to contribute to the design and implementation of robust data models (e.g., dimensional modeling) for analytics and reporting.
• A pragmatic problem-solver who can analyze and refactor complex legacy syst