Lead Data Engineer – AI Systems (Snowflake / Dbt / LLM)

Title: Lead Data Engineer – AI Systems (Snowflake / dbt / LLM) Location: Dallas, TX (Hybrid) Contract US Citizens Only About the Role Rockwoods is hiring a Lead Data Engineer for a high-visibility engagement with an insurance client. We are looking for someone who has genuinely worked on modern cloud data platforms and supported AI/LLM-driven initiatives in production environments. This is not a traditional ETL or reporting role. We need an engineer who understands how scalable data systems power AI applications — including LLM integrations, semantic search, vector-based retrieval, AI-ready data modeling, and production-grade pipelines. You should be someone who: • enjoys solving messy real-world data problems • can build and optimize systems hands-on • understands performance, scale, and reliability • has worked beyond proof-of-concepts and actually deployed solutions This is a strong opportunity for senior engineers who want ownership, technical influence, and meaningful architecture work. Responsibilities • Build and optimize scalable Python + Snowflake + dbt pipelines supporting analytics and AI use cases • Design modern data architectures for LLM workflows, RAG patterns, semantic search, and AI-enabled applications • Develop API and event-driven ingestion frameworks for structured and unstructured data • Improve platform reliability, observability, data quality, and performance • Prepare high-quality datasets for AI/ML inference and downstream applications • Tune Snowflake performance and optimize transformation efficiency/costs • Partner closely with engineering and business teams to solve operational data challenges • Help establish scalable engineering standards and modern data platform best practices Required Experience • 7+ years of hands-on Data Engineering experience • Strong expertise in Python, Snowflake, SQL, and dbt • Experience building production-grade pipelines and modern cloud data platforms • Experience supporting AI/LLM-related workflows in real environments • Hands-on experience with OpenAI, Anthropic, embeddings, vector search, semantic retrieval, or RAG-style architectures • Strong orchestration experience with Airflow or similar tools • Experience handling imperfect enterprise-scale data • Strong understanding of data modeling, optimization, transformation strategies, and scalability • Ability to work independently in a fast-moving engineering environment Strong Plus • Insurance domain experience (Claims, Policy, Billing, Underwriting, etc.) • Experience with vector databases or AI search architectures • Exposure to MLOps or AI deployment workflows • Experience designing reusable enterprise data frameworks What This Role Is NOT This is NOT: • a junior ETL developer role • a reporting/dashboard-only role • an AI “prompt engineering” role • a heavily bureaucratic environment with layers of approvals We are looking for builders and problem-solvers. Why Engineers Like This Role • Modern cloud + AI-focused tech stack • High ownership and technical influence • Direct impact on real business initiatives • Strong engineering culture • Fast interview process • Less process, more execution • Opportunity to shape architecture decisions early Important Please apply only if you have hands-on experience with modern Data Engineering AND practical AI/LLM-related implementations in production environments. Candidates with only reporting/dashboard backgrounds or purely academic AI exposure will likely not be a fit.