Senior Data Engineer (AI & Cloud Platforms) in United

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.

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• 7+ years of hands-on Data Engineering experience. \n

• Core Tech Stack: Strong mastery of Python, Snowflake, SQL, and dbt. \n

• AI/LLM Experience: Hands-on experience supporting AI/LLM workflows (Open AI, Anthropic, embeddings, vector search, semantic retrieval, or RAG architectures). \n

• Orchestration: Hands-on experience with Airflow or similar orchestration engines. \n

• Production Focus: Proven track record of building scalable platforms and handling imperfect enterprise data at scale. \n

• Autonomy: Ability to lead architectural decisions and work independently in a fast-moving environment.

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• Enjoy solving messy, complex, real-world data problems. \n

• Build and optimize scalable systems hands-on. \n

• Understand performance, scale, and reliability inside out. \n

• Have moved beyond proof-of-concepts and deployed production-grade solutions. \n

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If you want strong technical influence, architecture ownership, and the opportunity to build modern AI-ready data infrastructure, we’d love to connect.

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What You’ll Do

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• Build & Scale: Architect and optimize scalable Python + Snowflake + dbt pipelines supporting both analytics and AI production use cases. \n

• AI Architecture: Design modern data architectures for LLM workflows, RAG patterns, semantic search, and AI-enabled applications. \n

• Ingestion Frameworks: Develop robust API and event-driven ingestion frameworks for structured and unstructured data. \n

• AI Readiness: Prepare high-quality, curated datasets optimized for AI/ML inference and downstream consumption. \n

• Performance & Costs: Fine-tune Snowflake performance, optimize transformation efficiency, and keep compute costs low. \n

• Reliability & Quality: Improve overall platform reliability, observability, and data quality standards. \n

• Collaboration & Leadership: Partner with engineering and business teams while establishing modern engineering standards and best practices. \n

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