Data Engineer Sports Analytics Jobs in Utah (NOW HIRING)

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 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 architecture and engineering best practices.

What You Need to Get the Job Done

(If you don't have everything, we still encourage you to apply.)

• Ability to translate business problems into scalable data, AI, and knowledge engineering solutions.
• Experience working cross-functionally with technical and non-technical stakeholders.
• Ability to quickly learn new business domains and emerging analytics and AI technologies.
• Strong communication skills with the ability to explain complex technical concepts

Core Technical Skills

• Hands-on experience building AI agents and agentic workflows.
• Expert-level Python development for building scalable data, AI, and knowledge engineering solutions.
• Advanced SQL development and query optimization across transactional, analytical, and semantic data stores.
• Strong experience with Databricks, Spark, and distributed data processing frameworks.
• Experience designing and implementing scalable data pipelines for both structured and unstructured enterprise data using Databricks and PySpark.
• Deep understanding of lakehouse, data warehouse, semantic layer, and enterprise knowledge architecture principles.
• Experience designing canonical data models, semantic models, ontologies, taxonomies, and reusable domain-oriented data products.
• Strong understanding of metadata management, data cataloging, lineage, governance, and data discovery practices.
• Experience developing document ingestion, enrichment, chunking, and indexing pipelines to support AI-powered search and retrieval.
• Familiarity with embedding generation, vector indexing, and semantic retrieval concepts for Retrieval-Augmented Generation (RAG) systems.
• Experience with cloud-native data platforms (AWS preferred) and modern storage architectures.
• Experience implementing Infrastructure as Code (Terraform or similar), CI/CD pipelines, and automated deployment practices.
• Experience building observable, secure, and resilient data platforms with monitoring, testing, and operational best practices.
• Proficiency with Git-based development workflows and collaborative software engineering practices

Beyond technical skills, we're looking for someone who is:

• A systems thinker who enjoys connecting data, knowledge, and AI.
• Passionate about building trusted enterprise knowledge that powers intelligent experiences.
• Curious about emerging AI architectures and rapidly evolving technologies.
• Analytical and pattern-oriented.
• Creative in designing scalable data and AI solutions.
• Detail-oriented and persistent in solving complex engineering challenges.
• Comfortable working in a fast-paced, collaborative environment.
• Committed to continuous learning and engineering excellence.
• Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, or a related quantitative field (or equivalent practical experience).

What Will Make Us REALLY Love You

• Experience designing and evolving enterprise-scale data platform architectures.
• Experience working with, developing, and deploying MCP servers.
• Experience implementing event-driven architectures, streaming data pipelines, and Change Data Capture (CDC) technologies.
• Experience with Infrastructure as Code (Terraform, CloudFormation, or similar) and cloud automation.
• Experience building and operationalizing machine learning pipelines for training, validation, deployment, monitoring, and observability.
• Familiarity with enterprise data governance, metadata management, and data stewardship practices and tools.
• Experience implementing data security, privacy, and regulatory compliance frameworks.
• Experience supporting real-time analytics, low-latency data processing, or AI inference systems.
• Familiarity with common business metrics and data models across finance, sales, marketing, product, customer success, and operations.

What You'll Love About Us

• A Great Company Culture that has been recognized by multiple organizations like Inc, and Salt Lake Tribune
• Comprehensive health, life, and disability insurance
• Generous leave policies that include 4 weeks of vacation, 12 company holidays, parental leave, and volunteer time off so you can enjoy quality of life
• 401k plans with up to 6% company match
• $2000 Paid-Paid Vacation bonus
• EAP through Headspace
• Check out all our benefits that benefit you