Data Engineer, AI

Focused•Denver, CO

2d•$140,000 - $170,000•Hybrid

About The Position

Focused is seeking a Data Engineer to build the data foundation behind an enterprise AI platform for a global enterprise client. The role focuses on making clean, governed data available for retrieval by agents and LLM applications, primarily using Databricks and with a strong emphasis on AI use cases. The position involves building data pipelines and lakehouse models for analytics and AI, implementing governance and access controls, and ensuring data quality and pipeline reliability. It also includes preparing unstructured data for RAG systems, managing embeddings and vector search, and developing datasets to evaluate LLM retrieval quality. The engineer will partner with AI and platform engineers and deploy data infrastructure on Azure and/or GCP using software engineering practices such as version control, testing, and CI/CD. The role requires working from the Denver office three days per week and up to 20% travel within the United States. Focused cannot sponsor or take over sponsorship of employment visas for this role. The Denver base salary range is $140,000–$170,000.

Requirements

At least 5 years of experience in data engineering.
Strong hands-on experience with Databricks, Spark, and Delta Lake.
Proficiency in Python and SQL.
Experience building production data pipelines in a cloud environment; Azure or GCP experience is preferred.
Experience with data modeling and data quality practices.

Nice To Haves

Experience building data pipelines for RAG, embeddings, or vector search.
Experience with Unity Catalog and Mosaic AI.
Experience with streaming technologies such as Structured Streaming, Kafka, or Event Hubs.
Consulting or client-facing experience.
Experience improving an AI system's answers by fixing the underlying data.
Experience handling messy unstructured data at scale.
Databricks certifications or open-source contributions.

Responsibilities

Design and build batch and streaming pipelines in Databricks using Spark, Delta Lake, and Workflows.
Model data in a lakehouse architecture serving analytics and AI workloads.
Implement governance and access controls with Unity Catalog.
Monitor data quality and pipeline reliability.
Build ingestion and chunking pipelines for unstructured data, including documents, PDFs, and knowledge bases, to feed RAG systems.
Generate, store, and refresh embeddings, and operate vector search using Databricks Vector Search or similar tools.
Build datasets for LLM evaluation and help teams measure retrieval quality.
Partner with AI and platform engineers to ensure agents have appropriate data and permissions.
Deploy data infrastructure on Azure and/or GCP using infrastructure as code.
Apply software engineering practices to data work, including version control, testing, and CI/CD.