AI Platform Architect & Lead Enterprise Data, Analytics

Role : AI Platform Architect & Lead Enterprise Data, Analytics   

Location: Kansas City, MO (Local Candidates Preferred)
Industry: Engineering, Procurement & Construction (EPC)

Must Built modern Google Cloud Platform Data Platforms centered on BigQuery

Role Overview

We are seeking a senior leader to drive the vision, architecture, and delivery of the client's next-generation Enterprise Data, Analytics, and AI ecosystem. The ideal candidate will blend strategic consulting, enterprise architecture, data platform engineering, and AI solution delivery expertise to establish a scalable, business-aligned data and AI foundation.

This leader will be responsible for defining and implementing the enterprise Data & AI strategy, designing and standing up modern data platforms, enabling domain-oriented Data Products using Data Mesh principles, establishing semantic layers, and accelerating business value through advanced analytics and AI use cases.

The candidate should possess deep expertise in the Google Cloud Platform ecosystem, experience with Databricks Lakehouse and Palantir, and a strong understanding of EPC business processes and operational data domains.

Key Responsibilities

Enterprise Data, Analytics & AI Strategy

• Define Enterprise Data, Analytics, and AI Strategy aligned with business objectives.
• Develop multi-year Data & AI roadmaps, platform modernization strategies, and governance frameworks.
• Define operating models for enterprise-scale Data Products and AI Products.
• Establish business-case-driven investment strategies and value realization frameworks.
• Collaborate with business leaders to identify and prioritize high-value analytics and AI opportunities.

Data Platform Architecture

• Architect and implement enterprise-scale modern data platforms on Google Cloud.
• Define enterprise data architecture standards, reference architectures, and integration patterns.
• Lead platform modernization initiatives spanning cloud, analytics, AI, governance, and metadata management.
• Design scalable architectures supporting structured, unstructured, and operational workloads.

Data Mesh & Data Products

• Lead adoption of Data Mesh architecture and domain-driven platform design.
• Establish frameworks for:
  • Domain ownership
  • Self-service data platforms
  • Federated governance
  • Data Product lifecycle management
• Design and operationalize enterprise Data Products consumed by business, analytics, and AI teams.
• Drive product thinking and business outcome focus across data domains.

Semantic Layer & Business Modeling

• Define enterprise semantic modeling strategy enabling business-friendly consumption of enterprise data.
• Design semantic layers supporting governance, interoperability, self-service analytics, and AI.
• Hands-on experience with:
  • Timbr.ai
  • Business Ontologies
  • Knowledge Graphs
  • Semantic Modeling
  • Enterprise Metadata Management
• Partner with business stakeholders to establish common business definitions and KPI frameworks.

AI & Advanced Analytics

• Lead identification, design, and implementation of enterprise AI and GenAI use cases.
• Develop AI adoption frameworks, AI operating models, and responsible AI practices.
• Drive AI-powered business capabilities across engineering, procurement, project management, construction operations, and corporate functions.
• Partner with business stakeholders to industrialize AI solutions from pilot through production.

AI for Data & Analytics SDLC

Experience leveraging AI to modernize and accelerate the Data & Analytics delivery lifecycle through:

• AI-assisted development
• Automated code generation
• Data engineering productivity acceleration
• Data quality automation
• Test case generation
• Metadata-driven development
• Documentation generation
• Data model generation
• Automated pipeline migration and modernization
• Development copilots and engineering agents

Experience with AI-enabled transformation of data engineering and analytics delivery organizations is highly desirable.

Required Technical Qualifications

Primary Technology Stack (Must Have)

Google Cloud Platform (Expert Level)

• BigQuery
• Cloud Storage
• Dataproc
• Dataflow
• Pub/Sub
• Cloud Composer
• Dataplex
• Data Catalog
• Vertex AI
• IAM & Security Architecture
• Google Cloud Platform Data Governance Services

Strong experience designing and implementing enterprise-scale Google Cloud Platform data and AI platforms centered around BigQuery.

Data Platform Modernization

• Enterprise Data Platform Architecture
• Cloud Data Warehousing
• Lakehouse Architectures
• Data Governance
• Metadata Management
• Data Quality Frameworks
• Enterprise Information Architecture

Data Mesh & Data Products

Proven experience standing up:

• Enterprise Data Platforms
• Domain-Oriented Data Products
• Data Mesh Operating Models
• Federated Governance Models
• Self-Service Data Ecosystems

Semantic Modeling

• Timbr.ai (preferred)
• Semantic Layer Design
• Ontology Modeling
• Enterprise Business Models
• Knowledge Graph Concepts
• Metrics and KPI Modeling

BI & Analytics

Hands-on experience with:

• Looker
• LookML
• Looker Studio
• Power BI
• Executive Analytics Platforms
• Self-Service Analytics Enablement

Secondary Technology Stack

Databricks Lakehouse

Strong experience with:

• Delta Lake
• Unity Catalog
• Medallion Architecture
• Lakehouse Design Patterns
• Data Engineering Workloads
• AI/ML Workloads on Databricks

Experience integrating Databricks and BigQuery environments preferred.

Palantir

Experience with:

• Palantir Foundry
• Ontology-oriented Platforms
• Operational Analytics
• Data Product Enablement
• Digital Twin and Operational Decision Support Architectures

Domain Expertise (Mandatory)

Engineering, Procurement & Construction (EPC)

Deep understanding of:

• Engineering Data Domains
• Procurement Operations
• Supply Chain & Vendor Management
• Capital Project Delivery
• Construction Operations
• Project Controls
• Asset Lifecycle Management
• Cost & Schedule Management
• Program Management Office Processes

Experience delivering enterprise data or AI solutions within EPC environments is strongly preferred.

Leadership & Consulting Experience

• 15+ years of AI/ML Data & Analytics experience.
• 5+ years leading Enterprise Data & AI transformation programs.
• Executive stakeholder engagement up to CIO, CDO, CTO, and Business Leadership levels.
• Experience leading global architecture and delivery teams.
• Ability to bridge business strategy and technical implementation.
• Strong consulting, facilitation, roadmap development, and value realization skills.

Ideal Candidate Profile

The ideal candidate is a Data & AI Strategist, Enterprise Architect, and Platform Transformation Leader who has successfully:

✅ Defined Enterprise Data & AI Strategies
✅ Built modern Google Cloud Platform Data Platforms centered on BigQuery
✅ Implemented Data Mesh and Data Product operating models
✅ Established semantic layers using Timbr.ai or similar technologies
✅ Delivered Analytics, AI, and GenAI solutions at scale
✅ Leveraged AI to accelerate Data & Analytics SDLC and engineering productivity
✅ Worked within EPC organizations and understands industry-specific data domains
✅ Has hands-on experience with Databricks, Palantir, Looker, and Power BI
✅ Is located in or willing to work onsite in the Kansas City area