Principal Consultant Data Architect

As a Principal Consultant, Data Architect in IBM Consulting's Data & AI practice, you own end-to-end solution architecture for enterprise data and AI platforms across a portfolio of client engagements. You are the senior technical authority on the platforms we deliver: you set the target-state architecture, define the standards and reference patterns that engagement teams build to, review their designs, and hold the technical outcome through delivery.

Snowflake is the primary platform;
Databricks and open table formats are secondary.

This role sits one level above the engagement-aligned Solution Data Architect. Where that role owns one client's platform, this role owns the architecture practice across engagements: it governs engagement architects, resolves cross-engagement design questions, supports solutioning and pre-sales, and advises client executives on platform strategy and AI readiness. The role leads through technical authority and mentorship rather than line management, and thrives in a consulting environment where no two engagements are the same.

This role can be performed from anywhere in the US.

• Own end-to-end target-state architecture for enterprise data and AI platforms across concurrent engagements, with Snowflake as the primary platform and Databricks as secondary.
• Define account topology, environment strategy, security and governance model (RBAC, masking, row access, tagging), and cost and performance architecture at Snow Pro Advanced:
  Architect depth.
• Design layered data models (medallion, dimensional, Data Vault) and semantic layers that serve BI, application, and AI consumption.
• Select and justify integration patterns (CDC, event-driven, file-based, API, data sharing) against client constraints, and document the trade-offs.
• Set adoption direction for Cortex AI, Iceberg and open table formats, Snowpark, and data sharing, and define where Databricks or other lakehouse components fit alongside Snowflake.

• Author and maintain the practice's reference architectures, decision records, and reusable patterns for ingestion, transformation, governance, and AI‑ready data.
• Define engineering standards for dbt (or equivalent) model design, materialization, testing, and documentation, and for CI/CD and infrastructure as code across engagements.
• Run architecture reviews for engagement-level architects and lead engineers; approve or redirect designs before build.
• Contribute accelerators, estimation models, and enablement content back to the practice.

• Serve as senior technical authority to client executives: present and defend architecture decisions, roadmaps, and platform investment cases.
• Lead architecture assessments, AI readiness and data maturity evaluations, and translate findings into phased roadmaps.
• Surface architectural risk, scope drift, and technical debt early with proposed resolutions; partner with project and practice leadership on delivery health.

• Provide architecture, effort estimates, staffing shapes, and technical narrative for proposals and statements of work.
• Lead technical discovery and solution design in pursuit cycles alongside sales and practice leadership.

• Set technical direction for engagement teams; lead design and code reviews; hold quality of what ships against the approved architecture.
• Mentor engagement architects, data engineers, and analytics engineers; grow the practice's architecture bench.
• Drive enablement on Snowflake, Databricks, dbt, CI/CD, and AI‑assisted engineering practices.
• Use AI tooling in design and delivery work and set standards for its use within engagement teams.