Principal Consultant Data Architect (US Based)

IBM; Kansas City (MO); Remote; USD 180,000 - 240,000; Full time

Job summary

International Business Machines Corporation (IBM) Consulting is seeking a Principal Consultant, Data Architect to own end-to-end architecture for enterprise data and AI platforms across multiple client engagements. Snowflake is the primary platform, with Databricks and open table formats as secondary options.

The role leads through technical authority and mentorship, guiding engagement architects, data engineers, and analytics engineers while collaborating with client executives on platform

Qualifications

• Senior technical authority on enterprise data and AI platforms.
• Own end-to-end target-state architecture across concurrent engagements.
• Define standards and reference patterns for engagements.
• Mentor engagement architects and engineers.

Responsibilities

• Own end-to-end target-state architecture for enterprise data and AI platforms (Snowflake primary).
• Define environment strategy, security, governance, and cost/performance architecture at Architect depth.
• Design layered data models and semantic layers for BI, AI, and applications.
• Set adoption direction for Cortex AI, Iceberg, and open table formats; define fit of Databricks with Snowflake.

Skills

Snowflake

Databricks

AI readiness

Solution architecture

Tools

dbt

CI/CD

infrastructure as code

Snowpark

Job description

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.

Solution Architecture Ownership

• 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 SnowPro 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.

Reference Architecture and Standards

• 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.

Client and Executive Engagement

• 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.

Solutioning and Pre-Sales Support

• 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.

Technical Leadership and Enablement

• 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.