Engineer II – Data Engineer

GEICO GEICO · Insurance · Palo Alto, CA

GEICO is seeking an experienced Engineer II - Data Engineer to build and maintain scalable, resilient data pipelines for their Finance Data Warehouse within their FinTech organization. The role involves full data lifecycle collaboration, defining pipeline patterns, and leveraging AI models for SQL and Python code generation, while ensuring code quality and adhering to Agile methodologies and modern developer tooling.

What you'd actually do

  1. Scope, design, and build scalable, resilient data pipelines (orchestration, transformation, delivery) that support analytics and downstream products.
  2. Use modern developer tooling effectively, including AI-assisted coding (e.g., Cursor, GitHub Copilot) to accelerate delivery while maintaining code review, testing, and governance (no secrets in prompts or code, repo-aligned patterns).
  3. Engage in cross-functional collaboration across the full data lifecycle with analysts, platform engineers, and product partners from requirements through production support.
  4. Participate in design sessions and code reviews with peers to improve correctness, performance, security, and operability of data systems.
  5. Define, create, and support reusable pipeline patterns and standards (e.g., layering, testing, incremental design, naming, documentation) from both business and technology perspectives.

Skills

Required

  • SQL
  • dbt
  • Python
  • data transformation
  • pipeline automation
  • data pipeline architecture
  • batch workflows
  • idempotency
  • data quality
  • error handling
  • backfills
  • data systems architecture
  • layering
  • modeling patterns
  • reliability
  • scaling
  • cost awareness
  • structured data interchange
  • APIs
  • file-based ingestion patterns
  • computer science fundamentals
  • Git
  • branching/review workflows
  • cloud data and orchestration services
  • Snowflake
  • Apache Airflow
  • continuous delivery
  • Infrastructure as Code
  • Agile environment
  • problem-solving
  • debugging

Nice to have

  • AI-assisted coding

What the JD emphasized

  • strong standards for data protection, reliability, and availability
  • shipping high-quality technology products and services
  • broad technical depth, comfortable across the stack