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.

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.

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 applications, 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.
• Design and implement Knowledge Graph and Graph RAG architectures to model relationships between enterprise entities, documents, people, products, customers, and business processes.
• Develop entity extraction, relationship extraction, ontology, taxonomy, and metadata enrichment pipelines to improve knowledge discovery.
• Translate business requirements into scalable data models, semantic models, knowledge schemas, ERDs, data flow diagrams, and analytics and AI-ready architectures.
• Design and manage cloud-based data and AI infrastructure (Databricks preferred), including development, staging, and production environments.
• Design evaluation frameworks for retrieval quality, grounding accuracy, hallucination reduction, answer relevance, and AI system performance.
• Partner with data governance to implement MCP servers, metadata management, data cataloging, lineage, governance, and access controls that improve discoverability and trust of enterprise knowledge.
• Participate in peer code reviews, pull requests, architecture reviews, and engineering standards.
• Document data pipelines, knowledge pipelines, AI architectures, semantic models, infrastructure, and operational procedures.
• Define infrastructure as code and support CI/CD pipelines for data, AI, and knowledge engineering systems.
• Ensure enterprise data privacy, security governance, and responsible AI practices.
• Continuously improve platform scalability, resilience, retrieval performance, and operational efficiency.
• Contribute to the evolution of enterprise data, AI, and knowledge platform…