Job Closed
This listing is no longer active.
COVU delivers a combination of technology, real-time service, and sales support to the insurance agency’s customers.
Senior Data Engineer
Location
United States
Posted
114 days ago
Salary
$120K - $180K / year
Seniority
Senior
Job Description
Senior Data Engineer
COVU
• Develop the Policy Journal: Be a primary builder of our master data solution that unifies policy, commission, and accounting data. • Ensure Data Quality and Reliability: Implement robust data quality checks and monitoring. • Build the Foundational Analytics Platform: Implement and enhance our new analytics framework. • Modernize Core ETL Processes: Refactor existing Java & SQL (PostgreSQL) based ETL system. • Implement Data Quality Frameworks: Build and execute automated data validation frameworks. • Collaborate and Contribute to Design: Partner with product managers and stakeholders to understand requirements.
Job Requirements
- 5+ years of experience in data engineering
- Expert-level proficiency in Python and SQL
- Strong experience with modern data stack technologies, including a cloud data warehouse (Snowflake or Redshift)
- A workflow orchestrator (Airflow is highly preferred)
- Hands-on experience with AWS data services (e.g., S3, Glue, Lambda, RDS)
- Experience in the insurance technology (insurtech) industry
- 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
- Ability to read and understand existing Java/Hibernate logic is a strong plus.
- Excellent communication skills and the ability to collaborate effectively with both technical and non-technical stakeholders.
Benefits
- Possible Equity
Related Guides
Related Categories
Related Job Pages
More Data Engineer Jobs
• Design, build, and maintain scalable data pipelines using Python, Apache Airflow, and Apache Spark • Analyze business and technical requirements and translate them into reliable, future-proof data solutions • Develop, validate, deploy, and support complex ETL/ELT pipelines at scale • Build clean, secure, and maintainable REST APIs following company standards • Implement real-time and batch processing solutions for diverse data sources • Develop reusable data engineering and AI frameworks for enterprise-wide adoption • Define and manage domain-based “source of truth” data models, ensuring scalability and end-to-end data lineage • Implement data governance practices, automate data quality checks, and enable pipeline testing • Optimize data infrastructure for performance, cost efficiency, and reliability • Manage source control, CI/CD pipelines, and production deployments • Partner with data scientists and analysts to support AI/ML initiatives
Staff Data Engineer – Science
Exact SciencesExact Sciences is a publicly-traded molecular diagnostics firm focusing on early detection and prevention methods for some of the most life-threatening forms of
• Design, develop, and test multiple complex software applications • Collect and analyze data to develop robust IT solutions • Develop database architectures to address business requirements • Design and document database applications such as interfaces, data transfer mechanisms, and data partitions • Exercise independent judgment in methods, techniques, and evaluation criteria for obtaining results • Troubleshoot complex issues for major software applications • Act as a Technical Lead for your team • Collaborate with product teams for effective and efficient project objective
Snowflake Data Architect
DatavailWe help clients turn data into decisions no matter where it lives-in apps, on-prem, in a hybrid model, or in the cloud.
• Architect multi-tenant Snowflake environments for healthcare or life sciences. • Implement HIPAA/HITRUST-certified architectures and manage compliance. • Migrate sensitive healthcare datasets to the cloud without data leakage. • Collaborate with various teams on electronic health records and claims data management.
• Develop, maintain and optimize data pipelines (ETL/ELT) • Design and evolve modern data architectures • Process large volumes of data with Spark/PySpark • Ensure data quality, governance and security • Orchestrate pipelines using Airflow • Collaborate with data scientists, analysts and business stakeholders • Document solutions and share best practices • Perform troubleshooting, debugging and performance optimization




