Data Engineer

Coalition is the world's first Active Insurance provider designed to help prevent digital risk before it strikes. Founded in 2017, Coalition combines comprehensive insurance coverage and innovative cybersecurity tools to help businesses manage and mitigate potential cyberattacks.

Opportunities to make an impact with bold thinking are real—and happening daily at Coalition.

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.

• 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.

• Experience modeling insurance domains such as underwriting, claims, reinsurance, policy exposure, loss runs, or loss triangles.
• Familiarity with Snowflake Cortex, Feast, Data Hub, Atlan, Open Lineage, Elementary, dbt-expectations, or Evidently AI.
• Experience implementing RBAC hierarchies, row-level policies, column masking, PII tokenization, audit logging, or regulated-data controls.
• Experience managing Snowflake environments with Terraform and CI/CD tools such as Git Hub Actions.
• Experience using AI-assisted development tools such as Cursor or Git Hub Copilot to improve code quality, testing, and documentation.

As a remote-first organization, our compensation reflects the cost of labor across several…