Sr. Data Engineer

Please Note: This is a Utah-based hybrid position which will require some regular in-office days each week. Additionally, employment with BambooHR is contingent on passing both a background and credit check. AI at BambooHR At BambooHR, we’re all about setting people free to do great work, and we believe AI is a powerful partner in that mission. We’re leaning into intelligent tools to streamline our workflows, giving us more time for high-impact innovation. We look for curious, forward-thinking people who are ready to explore how AI can elevate their work and help us reimagine the future of HR. Essential Job Duties As a Senior Data Engineer, you will play a key role in designing, building, and operating scalable data platforms, analytics systems, AI/ML infrastructure, and the enterprise knowledge layer that powers intelligent applications and AI agents. You'll help extract, load, and transform structured and unstructured enterprise data into trusted, searchable, and reusable knowledge assets that enable retrieval-augmented generation (RAG), knowledge graphs, semantic search, AI agents, and advanced analytics. We’ll rely on your expertise across data, AI, and knowledge engineering to develop reliable systems that make organizational knowledge accessible at scale. Your ability to leverage AI to build performant data platforms, agentic workflows, and enterprise knowledge systems will be critical to your success. You will: Collaborate with data analysts, data scientists, ML engineers, business stakeholders, and AI engineers to enable trusted use of enterprise data and knowledge assets. Design, develop, and maintain scalable data pipelines using Python, SQL, PySpark, and modern data engineering frameworks. Build and optimize data lake, lakehouse, warehouse, data mart, and semantic data architectures. Design, build, and maintain an enterprise knowledge layer that unifies structured and unstructured information for AI and analytics workloads. Develop and maintain canonical data models, facts, dimensions, feature datasets, business entities, metadata models, and domain-specific data products. Design pipelines that ingest documents, knowledge bases, APIs, SaaS application s, event streams, and other enterprise content into analytics and AI-ready formats. Build pipelines for extracting, chunking, enriching, classifying, and embedding unstructured content. Design and manage vector databases and embedding pipelines to support semantic search and Retrieval-Augmented Generation (RAG). Build and optimize retrieval pipelines including hybrid search, metadata filtering, reranking, and context assembly.