Millions of records can still leave basic questions unanswered. Which records match? What's missing? Who can see the data? Before an AI Module can use a client's data, someone has to make that data reliable, usable, and secure. That's the work we're hiring for. IntoFocus AI builds governed AI Modules that turn deep subject-matter expertise into repeatable solutions for defined, valuable business problems across many clients. We're looking for a hands-on engineer to design, build, and secure the data foundation behind those Modules.
Location:
Jacksonville, FL. Hybrid, mostly in office, with some at-home sprint days.
Type:
Contract-to-hire.
Target start:
November 2026. The data comes from CRMs, ERPs, accounting systems, databases, APIs, and flat files. It can run to millions of records or more. Some of it is messy. Some of it is sensitive. You'll do the practical engineering work needed to bring it in, transform it, govern it, and serve it reliably and safely. What you'll work on: Build and maintain ETL and ELT pipelines for large client data sets. Map, clean, deduplicate, and reconcile records, then verify that the data is complete. Design reusable schemas and storage that work across many clients. Protect financial and personal data with least-privilege access, encryption, secrets management, client data separation, and logging. Put lineage, retention, validation, monitoring, and quality checks in place. Prepare and manage data for AI use, including retrieval sources, embeddings, and approved knowledge bases. Tune data systems for speed, infrastructure cost, and token cost. Document data flows, definitions, runbooks, and security controls. Work with our implementation lead and explain data risks in plain language. Client contact is occasional and usually alongside the implementation lead. We're looking for someone who has built data pipelines at volume and is comfortable working in SQL and Python. You should know how to handle complex migrations and reconcile messy data, model it for use beyond a single project, and apply practical security and privacy controls. Clear documentation and the ability to explain a problem without jargon matter here. Experience preparing data for AI or LLM systems is preferred. The core of the role, though, is data engineering: making the foundation sound enough for the work built on top of it. Apply through Indeed.