Engineer II – Data Engineer

At GEICO, we offer a rewarding career where your ambitions are met with endless possibilities. 

Every day we honor our iconic brand by offering quality coverage to millions of customers and being there when they need us most. We thrive through relentless innovation to exceed our customers’ expectations while making a real impact for our company through our shared purpose. 

When you join our company, we want you to feel valued, supported and proud to work here. That’s why we offer The GEICO Pledge: Great Company, Great Culture, Great Rewards and Great Careers.

Qualifications

Position Summary :

GEICO is looking for an experienced engineer who enjoys building fast, reliable platforms and applications that are easy to operate and designed for continuous availability. In this role, you will help advance our insurance business as we evolve into a technology organization grounded in engineering excellence. This role supports our Finance Data Warehouse.

Position Description :

As an Engineer II, you will be an important part of our FinTech organization, to uphold strong standards for data protection, reliability, and availability. Our team succeeds by shipping high-quality technology products and services in a high-growth setting where priorities can change quickly. We are looking for someone with broad technical depth, comfortable across the stack from user-facing experiences through backend and data systems and the integrations that connect them.

Position Responsibilities:

As an Engineer II - Data Engineer, you will: 

• Scope, design, and build scalable, resilient data pipelines (orchestration, transformation, delivery) that support analytics and downstream products.
• 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).
• Engage in cross-functional collaboration across the full data lifecycle with analysts, platform engineers, and product partners from requirements through production support.
• Participate in design sessions and code reviews with peers to improve correctness, performance, security, and operability of data systems.
• Define, create, and support reusable pipeline patterns and standards (e.g., layering, testing, incremental design, naming, documentation) from both business and technology perspectives.
• Leverage AI models to create SQL and Python, dbt (models, tests, macros, incremental strategies), Apache Airflow (DAGs, dependencies, backfill/retry patterns), cloud data warehouse platforms (e.g., Snowflake), and related integration patterns; then leverage their expertise to review and improve code quality.
• Execute delivery using an Agile methodology, continuous integration/continuous delivery, Infrastructure as Code where applicable, scripting for automation, platform consoles for warehouse and orchestration, and observability tooling (logging, metrics, alerting—for example dashboards and APM where used).
• Build pipeline definitions and apply strong technical judgment to choose and implement solutions that balance latency, cost, freshness, and reliability.
• Share best practices and improve processes within and across teams.

Qualifications:

• Strong hands-on experience with SQL, dbt and Python for data transformation and pipeline automation.
• Proven understanding of data pipeline architecture (batch workflows, idempotency, data quality, error handling, backfills) and how pipelines interface with a warehouse-centric analytics stack.
• Experience contributing to the architecture and design of data systems (layering, modeling patterns, reliability, scaling, cost awareness).
• Working knowledge of structured data interchange (e.g., JSON, XML/CSV as sources), APIs, and file-based ingestion patterns as used in analytics pipelines.
• Solid grounding in computer science fundamentals (e.g., complexity, joins, partitioning concepts) applied to data processing.
• Experience with Git tools and standard branching/review workflows.
• Familiarity with cloud data and orchestration services (e.g., Snowflake and managed Airflow or equivalent).
• Experience with continuous delivery and Infrastructure as Code for pipeline repos or supporting infrastructure.
• Strong oral and written communication skills.
• Strong problem-solving and debugging skills across SQL,