We're looking for a hybrid Data Architect who goes beyond traditional data acquisition to be 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.
What You'll Do
- 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.
What You'll Bring
- 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.
Benefits
- Competitive salary with performance-based bonus
- PTO, holidays, and sabbatical program
- Health, dental, vision, and life insurance
- Retirement plan with company match from day one
- Learning and professional development support
- Small-firm culture with direct access to leadership