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Introduction

A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You'll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, you'll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success. You'll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences.

Your role and responsibilities

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.

Required technical and professional expertise

- Bachelor's degree in Computer Science, Engineering, or an equivalent field.

- 10+ years in data engineering, warehousing, or solution architecture, including 4+ years owning end-to-end architecture on enterprise data platform engagements.

- Client-facing consulting or professional services delivery experience, including executive stakeholder engagement across concurrent engagements.

- 5+ years hands-on Snowflake: account and warehouse topology, clustering and performance, RBAC and security model, cost governance.

- Snowflake platform-native features: Streams, Tasks, Dynamic Tables, Snowpark, data sharing, external volumes and Iceberg tables, Cortex.

- Enterprise data modeling across medallion, dimensional, Data Vault, and 3NF approaches.

- dbt Core or Cloud: model design, materializations, macros, packages, testing, snapshots.

- CI/CD for data platforms (GitLab, GitHub Actions, or equivalent): pipelines, merge request workflows, environment promotion.

- Production experience on at least one major cloud (AWS, Azure, or GCP), including storage, IAM, networking and private connectivity, and secrets management.

- Experience architecting platforms that support AI and ML workloads, including vector and feature data, RAG patterns, and LLM integration in pipelines.

- Experience defining and enforcing architectural standards and reference architectures across multiple delivery teams.

- SQL and Python proficiency sufficient to review, prototype, and unblock delivery work.

- Demonstrated technical leadership: design and code review, mentoring architects and engineers, and setting technical direction.

- Track record contributing architecture and estimates to proposals and statements of work.

Preferred technical and professional experience

- Snowflake SnowPro Advanced: Architect certification; SnowPro Advanced: Data Engineer a plus.

- Databricks: Delta Lake, Unity Catalog, Spark; Databricks Certified Data Engineer Professional or Solutions Architect credential.

- Experience designing Snowflake and Databricks coexistence or migration architectures.

- Terraform for Snowflake and cloud infrastructure as code.

- Cloud architecture certification (AWS Solutions Architect Professional, Azure Solutions Architect Expert, or GCP Professional Data Engineer).

- Legacy platform migration leadership (Teradata, Oracle, SQL Server, Hadoop) including assessment, wave planning, and validation.

- Data governance and security architecture: catalog, li...

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