Staff Data Engineer, Coalition

• 9–12 years of experience building and operating production data platforms, with a track record of owning architecture for complex or high-scale systems
• Deep expertise in Snowflake and dbt, including performance optimization, incremental processing, warehouse cost management, testing, and deployment practices
• Strong programming and data modeling skills in Python and analytical SQL, with experience designing dimensional models and/or Data Vault 2.0 architectures
• Experience building reliable batch and streaming pipelines with modern orchestration and ingestion technologies such as Airflow, Dagster, Prefect, Kafka, Kinesis, Snowpipe Streaming, or Dynamic Tables
• Experience establishing data products, data contracts, domain ownership, SLAs, access controls, lineage, and quality standards across multiple teams
• Experience building feature engineering infrastructure or AI data systems, including feature stores, embeddings, vector search, or RAG pipelines
• Production experience with Looker and LookML, including governed semantic models and centralized metrics for self-service analytics
• A strong technical leadership mindset, with experience influencing architecture across teams, mentoring engineers, communicating tradeoffs, and creating durable engineering standards

Desirable

• As a Staff Data Engineer on Coalition’s Data team, you will define the architecture and technical direction of the enterprise Data & Analytics platform. You will build trusted, scalable data infrastructure for cyber security, insurance exposure analytics, BI, actuarial workflows, and AI/ML products. Your work will help Coalition make better risk decisions, deliver reliable data products, and scale its mission to Protect the Unprotected
• Set the architecture and engineering standards for Snowflake, dbt, orchestration, streaming, batch ELT, and infrastructure-as-code across the Data platform
• Design governed Medallion pipelines and domain-oriented data models that make complex insurance and security data reliable, discoverable, and usable
• Build reusable data products, contracts, feature stores, and semantic models that serve BI, actuarial, AI/ML, and operational consumers
• Establish observability, quality, lineage, security, and incident-management practices that protect data reliability and support regulated workflows
• Develop the platform foundations for feature engineering, embeddings, vector search, and retrieval-augmented AI applications
• Partner with engineering, product, underwriting, actuarial, security, and business teams to align architecture with customer and business outcomes