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.

• Cloud Integration: Architect integrations between the data platform and cloud services, including object storage, IAM, networking and private connectivity, secrets management, and managed processing services.

• Platform-Native and AI Capabilities: Evaluate and adopt platform-native capabilities where they fit the client's roadmap (for example Snowflake Cortex, Snowpark, Iceberg and open table formats, Databricks Unity Catalog), and design the data foundations that AI and ML workloads depend on.

• Delivery Ownership: Own technical delivery to production: decompose the architecture into workstreams, review what the team builds against it, monitor pipeline SLAs and platform cost, and lead root-cause analysis for data incidents.

• Client Engagement: Serve as the primary technical point of contact for client stakeholders and executives; lead design sessions and technical status reporting; surface risks, blockers, and scope changes early with proposed resolutions.

• Team Leadership and Mentorship: Set technical direction for data engineers and analytics engineers on the engagement, run design and code reviews, and develop teammates through pairing and structured feedback.

• Practice Contribution: Contribute reusable accelerators, patterns, and enablement content back to the practice, and support solutioning and estimation for new work.

• AI-Assisted Delivery: Use AI-assisted engineering tooling in design and delivery work and set standards for its use within the team.

This job can be performed from anywhere in the US.