Data Architect and Strategy - AI Integration & Deployment (Remote with travel - 1099 - Independent Contractor)

CompassX Group•Atlanta, GA

1d•Remote

About The Position

CompassX is a boutique business and technology consulting firm. We help Fortune 500 and high-growth clients deliver their most strategic initiatives, from enterprise transformations to digital and data-driven projects. With over 15 years of proven results, we’ve expanded across industries including financial services, pharmaceuticals, aerospace, consumer products, and quick service restaurants. We are honored to be recognized as a three-time winner of Consulting Magazine’s Best Boutique Firms to Work For, and previously recognized as a “Best Place to Work” in Southern California and one of INC.’s 5000 fastest-growing private companies in the U.S. We're looking for a Data Architect who is equal parts hands-on builder and trusted strategic advisor. You'll be embedded in a high-velocity GenAI application delivery engagement, owning the data architecture layer from design through to production — while also serving as the connective tissue between our delivery team and the client's data and engineering stakeholders. This isn't a slide-deck role. You'll be in the weeds of real data pipelines, modern lakehouse platforms, and AI-native application stacks - and then stepping back to advise senior client teams on what it all means for their data strategy. High preference will be given to candidates who are based in Georgia to occasionally co-locate with our team members there, with some travel to the client site. For employees: we are currently only set up to employ individuals who reside and work from the following states: California, Washington, Texas, Oregon, Colorado, Pennsylvania, Ohio, and Georgia . We are open to expanding this to other Midwest and East Coast states where applicable. For independent contractors: we have a strong preference for East Coast candidates who can work on a 1099 or C2C basis and are able to travel to client sites up to 50%.

Requirements

Senior Data Architecture Experience: 10+ years across data engineering, architecture, or a closely related discipline. You've designed systems that actually went to production — not just the whiteboard version.
Modern Data Platform Fluency: Deep familiarity with lakehouse architectures, with hands-on Databricks experience strongly preferred. You understand how data flows from raw ingestion to AI-ready serving layers.
GenAI Context: Experience working in or adjacent to AI/ML application development. You understand how LLMs and AI applications consume data, and what that demands of the underlying architecture.
Consulting & Stakeholder Acumen: Experience in a consulting or client-facing delivery environment. You can build trust quickly, navigate organizational complexity, and communicate architecture decisions to both technical and non-technical audiences.
Builder Mentality: You're not just an advisor — you write code, build pipelines, and validate your own designs. Python fluency and comfort in cloud-native data environments are expected.
AI-Ready Mindset: You embrace AI tooling as a force multiplier and are excited to operate at the intersection of modern data infrastructure and applied GenAI delivery.

Responsibilities

Architect the Data Foundation: Design and own the end-to-end data architecture underpinning GenAI application deployments - spanning ingestion, transformation, serving layers, and integration with existing enterprise data environments, including Databricks and legacy platforms.
Get Hands-On in the Stack: Work directly with application and ML teams to implement and validate data pipelines, schema designs, and data access patterns that power production-grade AI applications built in Python/FastAPI.
Bridge Two Worlds: Act as a high-EQ consultant who can translate complex data architecture decisions into clear strategic narratives for client leadership - and then roll up your sleeves to implement them alongside the team.
Set the Data Standards: Define and enforce data governance, quality, and lineage practices within the delivery environment. Ensure that what gets built is not just functional, but defensible, scalable, and ready for enterprise scrutiny.
Enable the Client: Partner with client-side data and engineering teams to build understanding and ownership of the new architecture. Leave behind capability, not dependency.