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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal Consultant, Data Architecture - **Company:** IBM - **Location:** Kansas City, MO, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Third Normal Form, Adobe InDesign, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, ARM Architecture, Microsoft Azure, Cloud Computing, Cloud Engineering, Code Review, Cyber Security, Continuous Integration, Data Architecture, Information Engineering, Data Governance, Data Sharing, Data Vault Modeling, Github, Apache Hadoop, Identity and Access Management, Python (Programming Language), Key Management, Microsoft SQL Server, Oracle (Applications), Role-Based Access Control, SQL Databases, Teradata SQL, Enterprise Data Management, Macros, Large Language Models, Snowflake, Apache Spark, Technical Debt, Gitlab, Data Lakes, AI Platforms, Information Technology, Data Management, Machine Learning Operations, Terraform, Databricks - **Published:** September 12, 2026 - **Apply:** https://dejobs.org/x/x/96E376CD85254480975770BEFABEE19E/job/ ## About the Role * 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, lineage, data quality frameworks, regulatory controls. * Regulated industry experience (insurance, financial services, healthcare, public sector). * MLOps and production AI operations on data platforms; agentic data workflows. * Master's degree. ## 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. ## Related Videos - [Crafting Custom Frameworks with Rust: A Deep Dive into Procedural Macros](https://www.wearedevelopers.com/videos/849-crafting-custom-frameworks-with-rust-a-deep-dive-into-procedural-macros) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [GitLab CI pipelines for a whole company](https://www.wearedevelopers.com/videos/143-gitlab-ci-pipelines-for-a-whole-company) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Modularity: Let's dig deeper](https://www.wearedevelopers.com/videos/1200-modularity-let-s-dig-deeper) - [Modern Data Architectures need Software Engineering](https://www.wearedevelopers.com/videos/1030-modern-data-architectures-need-software-engineering) ## Related Articles - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [What Are The Top Skills Required For Azure Developers?](https://www.wearedevelopers.com/magazine/77-what-are-the-top-skills-required-for-azure-developers) - [Best US AI Conferences for CTOs in 2026: Build vs. Buy, Vendor Evaluation, and Peer Intelligence](https://www.wearedevelopers.com/magazine/736-best-us-ai-conferences-for-ctos-in-2026-build-vs-buy-vendor-evaluation-and-peer-intelligence) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering)