Location: Remote
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 Do
- Build & 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
- Experience with specialized vector databases or AI search platforms.
- Exposure to MLOps or end-to-end AI deployment workflows.