Who we are:

At Focused, we move quickly to deliver quality software that achieves client outcomes and meets their customer's needs. We strategically partner with our clients to leverage our expertise in design and software, while our clients bring their own domain expertise. We work with a variety of clients from different industries, collaborating as we get new products to market, modernizing legacy systems, or helping teams learn the skills they need to be successful.

Our values:

• Listen first • We are experts in product practices but lifelong learners in the domain of our customers. We research, collaborate, and understand.
• Learn why • We ask questions and talk to users to understand problem spaces, objectives, and goals, which allows us to deeply invest and drive towards the outcomes of our clients.
• Love your craft • We love diving into a variety of domains and solving problems. We take pride in delivering value, in communicating progress, and guiding our clients to success.

We're looking for a Data Engineer to build the data foundation behind an enterprise AI platform for a global enterprise client. Agents and LLM applications are only as good as the data they can reach, and you'll own getting it there: clean, governed, and ready for retrieval. Most of your work will be in Databricks, with a strong lean toward AI use cases.

Key Responsibilities:

Data Pipelines & Platform

• Design and build batch and streaming pipelines in Databricks (Spark, Delta Lake, Workflows)
• Model data in a lakehouse architecture that serves both analytics and AI workloads
• Implement governance and access controls with Unity Catalog
• Monitor data quality and pipeline reliability

Data for AI

• Build ingestion and chunking pipelines for unstructured data (documents, PDFs, knowledge bases) that feed RAG systems
• Generate, store, and refresh embeddings; operate vector search (Databricks Vector Search or similar)
• Build datasets for LLM evaluation and help teams measure retrieval quality
• Partner with AI and platform engineers so agents get the right data with the right permissions

Cloud & Engineering Practices

• Deploy data infrastructure on Azure and/or GCP using IaC
• Bring software engineering habits to data work: version control, testing, CI/CD