AI/Data Architect: Data architect specializing in AI

Proactive REQ from Paul Reilly and specific to James Freire: Data architect specializing in AIAttendees:Paul Reilly, Director Enterprise Architecture (taking over for Ralph Holmes)Ralph Holmes: Senior Director, Enterprise Architecture (retiring)Andy Kearney: Director of DataMonday/Yesterday @ 2:16pm from Paul : The data team is going through some changes and the topic of another AI data architect arose. If that comes to fruition, I don't know if the position would reside on my team or under the data team, but I recall James seemed like a strong candidate so just curious if he happens to be available. If so, and he still falls into the general range we discussed, I can float his name.Today at 12:16pm from Paul : Good to hear. Can we line up an interview to reconnect with him either Wed at 1:30 or Thu at 11:00 or 3:00 this week to firm things up?From Paul at 12:30pm when I asked who would be on te interview and more detail about the role : Me, as I'm taking over for Ralph (though Ralph may join too) and likely Andy again on the Data side. It's an AI Data Architect role similar to the previous one, so he would help drive technical projects in both spaces, especially when they overlap. We are looking to do even more with AI and Data in general, and AI always has a strong dependency on data.Paul Reilly :Ralph Holmes :Andy Kearney :Current Size of Team: But this person will not be left on an island. They will be part of a team and have a lot of support from my team itself, because we interact with each other on everyone's projects anyway.Title & Reporting To: AI/Data Architect is undetermined and could report either to Andy (Data) or Paul (Dev)Their Direct Reports (if any): n/aLocation: REMOTE, But US Based > Preferred Time Zone - EST preferred and then CST. He has people in Europe.EST Hours: 8am to 5pm (core hours) but heavy emphasis in the mornings. Historically no OT or weekends so 40 hours are typical. Ralph also has team members in Eastern EuropeDuration: ?RequirementsThe candidate should have general data architecture skills. This includes:Relational data (SQL Server, MongoDB & Snowflake)NoSQL and other document-oriented data structures (MongoDB)Data lake, data warehouse, and data unification conceptsData governance concepts, including metadata management, lineage tracking, and data catalogsThe candidate should be familiar with data migration, replication, and ETL techniques.The candidate should be familiar with unstructured data (i.e., PDFs) and its application in AI.The candidate should be familiar with AI architecture. Knowledge of OpenAI or other LLM is required.The candidate should be familiar with RAG architecture, including the consumption and vectorization of data.SOFT SKILLS: Not leading people but bringing ideas to the room and being the "loudest voice" of those ideas.DAY to DAY: Working in teams of people and on multiple projects. Multiple Data related projects happening. E.g. Azure to AWS, something Snowflake, etc...Tech Environment: Azure, AWS, Snowflake, SQL Server, MongoDB, unstructured Data (e.g. PDFs, emails, text documents)Bill Rate: The funding comes from the AI budget. We can go up to 150/hr. ($300 k allocated for the year)= Ideal Candidate Summary:A hands-on Data Architect with experience designing modern data platforms that support AI initiatives. They should be equally comfortable working with structured and unstructured data, understand the backbone of large-scale data systems, and know how to make data usable for AI tools like OpenAI. Familiarity with RAG architectures and vector databases is key.> What's the customer looking for?They need a Data Architect — someone who can design and manage how a company's data is stored, moved, and made usable — with a strong focus on supporting AI initiatives.This person needs to be able to work with both traditional data systems (like databases and data warehouses) and newer AI-specific architectures (like OpenAI or RAG systems).Key Skills (in plain terms):1. General Data Architecture ExperienceMust know how to structure and organize data in databases:SQL-based (like SQL Server, Snowflake) — Think organized spreadsheets for structured data.NoSQL/Document-style (like MongoDB) — Think flexible, less structured formats for things like user profiles or documents.2. Big Picture Data SystemsUnderstands Data Lakes and Data Warehouses — systems where companies store massive amounts of data, either raw or cleaned.Can bring data together from different places into one unified system that's usable by the business.3. Data GovernanceKnows how to track where data came from, how it's being used, and how to keep it organized and documented (i.e., using data catalogs, metadata, lineage tracking).Important for security, compliance, and building trust in AI outcomes.4. ETL / Data MovementUnderstands ETL (Extract, Transform, Load) — how to move data from one place to another, clean it up, and get it ready for use.Also includes replication (copying data across systems) and migration (moving data from old systems to new ones).5. Unstructured Data HandlingNeeds to be comfortable working with messy, non-database formats like PDFs, emails, or text documents — common in AI use cases.6. AI Architecture & ToolsMust have experience building systems that support AI, especially with:OpenAI or similar LLMs (Large Language Models)RAG (Retrieval-Augmented Generation) — a newer AI architecture where the system looks up relevant data before generating an answer.Includes vectorization (turning words/documents into numerical formats so AI can understand them).