Senior Data Engineer (AI & Cloud Platforms)

Location: RemoteNeed U.S. Citizens

About the Role: Rockwoods is hiring a Senior Data Engineer for a high-visibility engagement with an insurance client. This is not a traditional ETL or reporting role. We are looking for a senior engineer who understands how scalable data systems power modern AI applications including LLM integrations, semantic search, vector-based retrieval, AI-ready data modeling, and production-grade pipelines. This role is for you if you: Enjoy solving messy, complex, real-world data problems. Build and optimize scalable systems hands-on. Understand performance, scale, and reliability inside out. Have moved beyond proof-of-concepts and deployed production-grade solutions. If you want strong technical influence, architecture ownership, and the opportunity to build modern AI-ready data infrastructure, we’d love to connect. What You’ll DoBuild & Scale: Architect and optimize scalable Python + Snowflake + dbt pipelines supporting both analytics and AI production use cases. AI Architecture: Design modern data architectures for LLM workflows, RAG patterns, semantic search, and AI-enabled applications. Ingestion Frameworks: Develop robust API and event-driven ingestion frameworks for structured and unstructured data. AI Readiness: Prepare high-quality, curated datasets optimized for AI/ML inference and downstream consumption. Performance & Costs: Fine-tune Snowflake performance, optimize transformation efficiency, and keep compute costs low. Reliability & Quality: Improve overall platform reliability, observability, and data quality standards. Collaboration & Leadership: Partner with engineering and business teams while establishing modern engineering standards and best practices.

What We’re Looking For: 7+ years of hands-on Data Engineering experience. Core Tech Stack: Strong mastery of Python, Snowflake, SQL, and dbt. AI/LLM Experience: Hands-on experience supporting AI/LLM workflows (Open AI, Anthropic, embeddings, vector search, semantic retrieval, or RAG architectures).Orchestration: Hands-on experience with Airflow or similar orchestration engines. Production Focus: Proven track record of building scalable platforms and handling imperfect enterprise data at scale. Autonomy: Ability to lead architectural decisions and work independently in a fast-moving environment.

Nice-to-Haves: Insurance domain expertise (Claims, Policy, Billing, Underwriting, etc.).Experience with specialized vector databases or AI search platforms. Exposure to MLOps or end-to-end AI deployment workflows. Experience designing reusable enterprise data frameworks.