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

Title: Lead Data Engineer – AI Systems (Snowflake / dbt / LLM)Location: Dallas, TX (Hybrid)ContractUS Citizens OnlyAbout the RoleRockwoods 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 problemscan build and optimize systems hands-onunderstands performance, scale, and reliabilityhas worked beyond proof-of-concepts and actually deployed solutionsThis is a strong opportunity for senior engineers who want ownership, technical influence, and meaningful architecture work.ResponsibilitiesBuild and optimize scalable Python + Snowflake + dbt pipelines supporting analytics and AI use casesDesign modern data architectures for LLM workflows, RAG patterns, semantic search, and AI-enabled applicationsDevelop API and event-driven ingestion frameworks for structured and unstructured dataImprove platform reliability, observability, data quality, and performancePrepare high-quality datasets for AI/ML inference and downstream applicationsTune Snowflake performance and optimize transformation efficiency/costsPartner closely with engineering and business teams to solve operational data challengesHelp establish scalable engineering standards and modern data platform best practicesRequired Experience7+ years of hands-on Data Engineering experienceStrong expertise in Python, Snowflake, SQL, and dbtExperience building production-grade pipelines and modern cloud data platformsExperience supporting AI/LLM-related workflows in real environmentsHands-on experience with OpenAI, Anthropic, embeddings, vector search, semantic retrieval, or RAG-style architecturesStrong orchestration experience with Airflow or similar toolsExperience handling imperfect enterprise-scale dataStrong understanding of data modeling, optimization, transformation strategies, and scalabilityAbility to work independently in a fast-moving engineering environmentStrong PlusInsurance domain experience (Claims, Policy, Billing, Underwriting, etc.)Experience with vector databases or AI search architecturesExposure to MLOps or AI deployment workflowsExperience designing reusable enterprise data frameworksWhat This Role Is NOTThis is NOT:a junior ETL developer rolea reporting/dashboard-only rolean AI “prompt engineering” rolea heavily bureaucratic environment with layers of approvalsWe are looking for builders and problem-solvers.Why Engineers Like This RoleModern cloud + AI-focused tech stackHigh ownership and technical influenceDirect impact on real business initiativesStrong engineering cultureFast interview processLess process, more executionOpportunity to shape architecture decisions earlyImportantPlease 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.