Sr. Consultant, Data Architect

We are looking for a Sr. Consultant, Data Architect to join our growing team of experts. This position owns the technical architecture of client engagements on modern cloud data platforms, with Snowflake and Databricks and cloud-native services on AWS, Azure, and GCP alongside it. The work spans target-state platform design, data modeling, ingestion and transformation patterns, security and governance design, and the engineering standards that keep delivery consistent from first design through production.

The ideal candidate is as comfortable whiteboarding an architecture as they are rolling up their sleeves in delivery. They have designed and delivered enterprise data platforms end to end, lead discovery with clients to turn business goals into technical requirements, and make and document the trade-offs behind every design decision. They incorporate AI tooling into how they work and deliver, can advise clients on where AI fits in their data strategy, and architect platforms that support AI and analytics workloads while keeping foundational data quality, structure, and governance intact.

This role carries technical leadership on an engagement. The Solution Architect is accountable for the technical outcome: the architecture is sound, the team builds to it, and what ships to the client meets the design. They serve as the primary technical point of contact for client stakeholders, set technical direction for the delivery team, run design and code reviews, and mentor engineers at earlier stages of their careers. This person thrives in a consulting environment where no two engagements are the same.

This role can be performed from anywhere in the US.

Responsibilities:

• Discovery and Solution Design: Lead discovery sessions with client stakeholders to clarify business goals, technical requirements, and constraints, and translate them into a production-ready data platform architecture with a phased delivery plan.

• Platform Architecture: Design platform topology, environment strategy, security and governance model (role-based access, masking, row-level policies, tagging), and cost and performance architecture for the engagement's platform.

• Data Modeling: Design layered data models (medallion, dimensional, Data Vault) and semantic layers that serve BI, application, and AI consumption.

• Integration and Pipeline Patterns: Select integration patterns (batch, CDC, event-driven, file-based, API, data sharing) against client constraints, and document the trade-offs in architecture decision records.

• Transformation and Engineering Standards: Lead model design, materialization strategy, testing, and documentation in dbt or an equivalent framework; establish CI/CD, coding standards, and peer review for the team.