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Mid-level Data Engineer title, metro locations, and broad AWS/Snowflake/Airflow skillset increase candidate competition.
Core data engineering skills are broadly transferable; insurance experience is only a nice-to-have.
Multiple mandatory technical skills plus a 4+ years requirement enforce strict shortlisting filters.
Build and maintain scalable and reliable data platforms and pipelines powering analytics and data products across the company.
Manage and provision AWS data infrastructure components (S3, IAM, Lambda, RDS/Aurora) and Snowflake resources using Terraform, following established modules and patterns.
Implement observability, data quality checks, lineage, governance, and troubleshooting for data pipelines and platform infrastructure.
4+ years of experience in Data Engineering with focus on data platform and pipeline development.
Hands-on experience with AWS core services including S3, IAM, and compute services.
Strong proficiency in SQL and Python for data transformations and pipeline creation.
Experience with cloud data warehouses (e.g., Snowflake) and orchestration tools like Airflow (including managed Airflow).
Experienced working within agile, cross-functional teams alongside senior engineers, analysts, and data scientists for collaborative delivery.
Demonstrates strong data modeling skills (dimensional and fact-based designs) for scalable data platform needs.
Familiarity with ELT tools like dbt (including dbt Cloud), Terraform-based infrastructure as code, containerized services (Docker, Kubernetes/EKS), and streaming/event-driven data platforms.