[Hiring] Managing Consultant, Data Architect @IBM

Role Description

We are looking for a Managing Consultant, Data Architect to join our growing team of experts. This position is the principal technical authority for enterprise data and AI platforms across a portfolio of client engagements, with Snowflake and Databricks and cloud-native services on AWS, Azure, and GCP alongside it. The work spans:

• Target-state architecture and roadmaps
• Data modeling and integration patterns
• Security and governance architecture
• Reference architectures and standards

The ideal candidate leads architecture through delivery rather than handing off a design. They define the target state, decompose it into workstreams, make and document the trade-offs, and review what engagement teams build against it. They have been the lead architect on multiple enterprise platform implementations, lead discovery and executive advisory with client leadership, and bring a business-first lens to every design decision. They incorporate AI tooling into how they design and deliver, advise clients on AI readiness and where AI fits in their data strategy, and architect platforms that support AI workloads while keeping foundational data quality, structure, and governance intact.

This role carries principal-level ownership and a very high bar for delivery. The Managing Consultant, Data Architect is accountable for the technical outcome across every engagement they govern:

• The architecture is sound
• The standards are followed
• What ships to the client meets the design
• Client executive sponsors trust them to make and defend the hard technical calls

They set technical direction for engagement architects and lead engineers, run architecture reviews, author the practice's reference architectures, lead solutioning and pre-sales for new work, and grow the practice's architecture bench through mentorship. They lead through technical authority rather than line management and thrive in a consulting environment where no two engagements are the same.

This role can be performed from anywhere in the US.

Responsibilities

• Target-State Architecture: Own end-to-end target-state architecture for enterprise data and AI platforms across concurrent engagements, including platform topology, environment strategy, security and governance model, and cost and performance architecture at advanced-certification depth.
• Roadmaps and Executive Advisory: Lead architecture assessments and AI readiness and data maturity evaluations; translate findings into phased roadmaps with success metrics, investment cases, and cost guardrails; present and defend them to client executives.
• Data Modeling and Integration Patterns: Design layered data models (medallion, dimensional, Data Vault, 3NF) and semantic layers; select and justify integration patterns (batch, CDC, event-driven, API, data sharing) and record the trade-offs as architecture decision records.
• Reference Architectures and Standards: Author and maintain the practice's reference architectures, decision records, and reusable patterns for ingestion, transformation, orchestration, observability, governance, and AI-ready data; define engineering standards for transformation frameworks, CI/CD, and infrastructure as code.
• Architecture Governance: Run architecture reviews for engagement architects and lead engineers; approve or redirect designs before build; resolve cross-engagement design questions.
• Platform Direction: Set adoption direction for platform-native and AI capabilities (for example Snowflake Cortex, Snowpark, Iceberg and open table formats, Databricks Unity Catalog and Mosaic AI) and define where each platform fits in a client's estate, including coexistence and migration architectures.
• Delivery Accountability: Hold the technical outcome through delivery: decompose the architecture into workstreams, review what ships against the approved design, surface architectural risk, scope drift, and technical debt early with proposed resolutions, and partner with project and practice leadership on delivery health.
• Solutioning and Pre-Sales: Own 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 Mentorship: Set technical direction for engagement teams; mentor architects, data engineers, and analytics engineers; support hiring, onboarding, and technical enablement for the practice.
• Practice and Partner Contribution: Contribute accelerators, estimation models, and enablement content; represent the practice with platform partners and in technical communities.
• AI-Assisted Delivery: Use AI tooling in design and delivery work and set standards for its use across engagement teams.

Qualifications

• Bachelor's degree in Computer Science, Engineering, or an equivalent field.
• 10+ years in data engineering, data warehousing, or solution architecture, including 4+ years owning end-to-end architecture on enterprise data platform engagements and serving as lead architect on at least three implementations.
• Client-facing consulting or professional services delivery experience, including executive stakeholder engagement and advisory across concurrent engagements.
• 5+ years hands-on at advanced depth on at least one modern cloud data platform: Snowflake or Databricks, with working knowledge of the other sufficient to design coexistence or migration architectures.
• Deep expertise in enterprise data modeling and target-state platform design across ingestion, transformation, semantic, and consumption layers.
• Proven track record designing batch and streaming ingestion frameworks and choosing among integration patterns based on client constraints.
• Strong proficiency in SQL and Python; hands-on with ELT and orchestration tooling.
• CI/CD and infrastructure as code for data platforms.
• Cloud platform experience on AWS, Azure, or GCP.
• Experience architecting platforms that support AI and ML workloads.
• Ability to assess client AI readiness and data maturity.
• Experience defining and enforcing architectural standards across multiple delivery teams.
• Demonstrated technical leadership.
• Track record of owning technical delivery across complex, multi-workstream engagements.
• Strong client-facing communication skills.
• Practical use of AI-assisted development tooling.
• Comfortable working in Agile delivery environments.

Requirements

• Platform Certifications: SnowPro Advanced: Architect; Databricks Certified Data Engineer Professional or Solutions Architect credential.
• Cloud Architecture Certification: AWS Solutions Architect Professional, Azure Solutions Architect Expert, or GCP Professional Data Engineer.
• Platform Migration Leadership: Leading migrations from legacy warehouses to modern cloud platforms.
• Data Governance and Security Architecture: Designing governance frameworks and data quality controls.
• Streaming and Event-Driven Architecture: Kafka, Kinesis, EventBridge, or Event Hubs.
• Applied AI in Production: MLOps practices and operating AI workloads on data platforms.
• FinOps for Data Platforms: Cost governance models and optimization programs.
• Industry Context: Regulated industry experience.
• Thought Leadership: Contributions to technical communities.