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.
Drive Proactive Integration: Operate at a high velocity by anticipating data needs rather than waiting for them. You will partner closely with—and complement—integration architects, filling the technical gaps to keep the project moving at a rapid pace
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.

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 Snowflake 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.
Application Development Exposure: You have a strong understanding of the full application development lifecycle. You possess the 'know-how' of app building to see exactly how data architecture integrates with the broader system.
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.

Data Architect and Strategy – AI Integration, Deployment

Key skills