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, and AI/ML infrastructure. We’ll rely on your expertise across data, analytics, ML, and AI engineering to develop, automate, and maintain pipelines and intelligent systems.

Your ability to use AI in building reliable, performant, and scalable data, ML, and AI systems— effectively building and leveraging AI agents and agentic workflows —will be critical to your success.

You will:

Collaborate with data analysts, data scientists, ML engineers, software engineers, and business stakeholders to enable effective use of core data assets

Design, develop, and maintain scalable data ingestion and transformation pipelines using Python, SQL, and modern data tooling

Build and optimize data lake, lakehouse, warehouse, and data mart architectures

Develop and maintain data models including facts, dimensions, feature datasets, and domain-specific data products

Translate business requirements into design documents (e.g., ERDs, data flow diagrams) data models and ML feature pipelines

Design and manage cloud-based data and ML infrastructure (Databricks preferred), including development, staging, and production environments

Design, build, and operationalize machine learning pipelines for training, validation, deployment, and observability (e.g., performance, drift, reliability)

Support ML model lifecycle management, including versioning, reproducibility, and lineage

Develop and maintain ML feature stores and reusable feature pipelines for ML models

Build and integrate AI-powered applications and agentic workflows (e.g., LLM-based agents, retrieval-augmented generation systems, workflow automation agents)

Design and implement data pipelines for AI systems, including unstructured data (text, logs, embeddings, vector stores)

Develop and maintain unit, integration, and data quality tests

Participate in peer code reviews, pull requests, and team coding standards

Document data pipelines, ML pipelines, models, infrastructure, and standard operating procedures

Define infrastructure as code and support CI/CD pipelines for data and ML systems

Ensure data privacy, security, and access control best practices (including AI data governance considerations)

Identify and implement improvements in efficiency, scalability, resilience, and performance

Contribute to evolving data, ML, and AI platform architecture, tools, and best practices

You’ll help power analytics, machine learning, and intelligent decision-making across domains such as finance, marketing, sales, product, and customer experience.

What You Need to Get the Job Done

Collaboration Business Engagement

Ability to gather requirements and translate business processes into data, ML, and AI solutions

Comfortable working cross-functionally with both technical and non-technical stakeholders

Ability to quickly learn new domains and technologies

Core Technical Skills

Strong Python development experience

Advanced SQL development and query optimization skills

Understanding of Databricks and large-scale data processing

Experience building and scaling data pipelines using Databricks and PySpark

Deep understanding of data lake, lakehouse, data warehouse, and data mart architectures

Experience with data modeling across a variety of business domains

Experience with modern data tooling (e.g., dbt or similar transformation frameworks)

Knowledge of data formats, data patterns, and modeling best practices

Experience with cloud platforms (AWS preferred)

Experience with CI/CD pipelines in a data engineering environment

Git-based development workflows

Bachelor’s degree in computer science, information systems, a quantitative field, or equivalent practical experience

AI, ML MLOps Skills

Hands-on experience using AI development tools and IDEs (e.g., Cursor, Copilot, Claude Code, or similar)

Experience building AI agents and agentic workflows

Exposure to LLMs, embeddings, vector databases, or generative AI systems

Familiarity with handling structured and unstructured data (e.g., text, logs, embeddings)

Experience building or supporting machine learning pipelines in production

Familiarity with AI and MLOps in Databricks

Experience with ML feature engineering and feature stores

Understanding of ML model lifecycle managem…