Lead Data & AI Architect - GCP

Job Title: Lead GCP Data AI Architect

Location: Charlotte, NC (Min 3 days onsite)

Work Model: Client-facing; minimum 3 days per week onsite

JD:

Lead Data & AI Architect – GCP

Role Overview:

We are seeking a highly experienced Lead Data & AI Architect & Strategy consultant with deep expertise in Google Cloud Platform (GCP) to lead the architecture for a strategic Data and AI transformation program within a leading financial-services organization. This is a critical technology leadership role with end-to-end responsibility for defining and governing the Data and AI architecture across the transformation lifecycle, from current-state discovery and assessment through target-state architecture, implementation planning and implementation. The role will establish the architectural direction, drive key technology decisions and provide technical leadership to ensure the platform is scalable, secure, resilient and capable of supporting enterprise-grade Data, AI and agentic AI use cases.

The role will work closely with senior stakeholders across Data, AI, Technology, Architecture, Security, Risk and Business functions, while providing technical direction to architects, engineers and delivery teams. The successful candidate will combine deep GCP expertise with strong enterprise Data and AI architecture, consulting and leadership experience, and will be expected to translate business objectives into pragmatic technology solutions and measurable outcomes.

Key Responsibilities

Architecture & Technology Leadership

• Own the end-to-end Data and AI architecture for the transformation program and establish the target-state technology vision.
• Lead current-state architecture assessments, identify technical gaps and dependencies, and define the target-state architecture and modernization strategy.
• Translate business and technology requirements into scalable, secure and implementation-ready architecture.
• Define architecture principles, reference architectures, technology standards and reusable patterns for enterprise adoption.
• Drive architecture decisions across data, AI, cloud, integration, security, governance and observability.
• Lead architecture governance, design reviews and technical decision-making throughout the program.

Data & GCP Architecture:

• Design and govern enterprise-scale data platforms on GCP supporting batch, real-time and streaming workloads.
• Architect data ingestion, processing, storage, transformation, serving and consumption patterns using GCP-native services.
• Provide deep architectural guidance across BigQuery, Cloud Storage, Dataplex, Dataflow/Apache Beam, Pub/Sub, Dataproc, Vertex AI and related GCP services.
• Define modern data architecture patterns, including lakehouse/Medallion architecture, enterprise data models and data products where appropriate.
• Design high-volume data workloads with appropriate approaches to performance, scalability, resilience, availability and cost optimization.
• Define enterprise integration patterns across applications, APIs, databases and other data sources.
• Establish BigQuery architecture and optimization strategies, including data modeling, partitioning, clustering and workload management.

AI, GenAI & Agentic AI Architecture:

• Define the architecture required to support enterprise AI/ML, Generative AI and agentic AI capabilities.
• Design the data, knowledge and platform foundations required for AI agents to securely access, retrieve and reason over enterprise information.
• Define architecture patterns for RAG, vector search, embeddings, knowledge retrieval and enterprise search.
• Architect integration between AI agents, enterprise data, APIs, tools and business applications to enable AI-driven business processes and outcomes.
• Define patterns for AI orchestration, multi-agent interaction, tool execution, human-in-the-loop workflows and agent observability where applicable.
• Establish architecture for model development, deployment, monitoring and lifecycle management using capabilities such as Vertex AI.
• Ensure AI solutions incorporate appropriate guardrails, security, governance, responsible AI and model-risk controls.

Security, Governance & Regulatory Architecture:

• Ensure architecture aligns with financial-services security, regulatory, privacy and risk requirements.
• Define secure architecture patterns covering IAM, RBAC, service accounts, encryption, network security, data access, environment isolation and segregation of duties.
• Incorporate data governance, classification, lineage, metadata, data quality, retention and auditability into the platform architecture.
• Partner with Security, Risk, Compliance and Governance teams to ensure architecture and implementation meet enterprise controls and standards.
• Establish appropriate governance and operational controls for AI/ML and agentic AI capabilities.

Implementation & Engineering Leadership:

• Translate the target-state architecture into a pragmatic implementation roadmap, including priorities, dependencies, sequencing and key technical decisions.
• Provide architectural direction to Data Engineering, AI Engineering, Cloud, DevOps and MLOps teams.
• Review and approve detailed solution designs, data models, pipelines, AI solutions and platform components.
• Ensure implementation remains aligned with the target architecture and established engineering standards.
• Lead resolution of complex technical issues and architecture trade-offs.
• Establish reusable architecture patterns, frameworks and accelerators to improve delivery consistency and speed.
• Ensure solutions meet defined requirements for performance, scalability, reliability, security, observability and cost.
• Mentor and develop architects and senior technical resources across the program.

Client & Stakeholder Leadership:

• Act as a trusted technology advisor to senior client stakeholders across Data, AI, Technology, Architecture, Security, Risk and Business functions.
• Lead architecture workshops, design sessions, technical reviews and executive discussions.
• Present architecture recommendations, trade-offs, risks and roadmap decisions to senior leadership and steering committees.
• Build alignment across business and technology stakeholders and influence decisions in complex, ambiguous environments.
• Communicate complex technical concepts clearly to both executive and technical audiences.
• Partner with delivery and program leadership to identify and manage architectural risks, dependencies and critical technical decisions.
• Take ownership for architectural outcomes and contribute directly to the overall success of the transformation program.

Required Skills & Experience:

• 12+ years of experience in Data, AI, Cloud or Enterprise Architecture, with significant experience leading large-scale technology transformation programs.
• Extensive experience designing and implementing enterprise-scale solutions on GCP.
• Deep expertise in GCP data and AI services, including BigQuery, Cloud Storage, Dataflow/Apache Beam, Pub/Sub, Dataproc and Vertex AI.
• Strong experience designing modern enterprise data platforms and architectures, including data lakes/lakehouses, data warehouses, data products and streaming platforms.
• Proven experience designing both batch and real-time/streaming data architectures.
• Strong experience with AI/ML architecture and practical experience with Generative AI and/or agentic AI architectures.
• Strong understanding of RAG, vector search, embeddings, knowledge architectures, AI orchestration and integration of AI agents with enterprise systems and data.
• Experience designing secure, governed and production-grade AI platforms.
• Strong understanding of data security, governance, privacy, lineage and data quality.
• Proven experience working within financial services, banking or other highly regulated industries.
• Experience leading architecture discovery, assessment and transformation engagements.
• Demonstrated ability to translate business requirements and current-state findings into target-state architecture and implementation roadmaps.
• Strong understanding of cloud scalability, resilience, performance, observability and cost optimization.
• Experience with DevOps/MLOps and productionization of Data and AI solutions.
• Excellent executive communication, stakeholder management and influencing skills.
• Proven ability to lead and mentor multidisciplinary architecture and engineering teams.

Preferred Qualifications:

• Google Cloud Professional Cloud Architect and/or Professional Data Engineer certification.
• Experience with production-scale agentic AI platforms and enterprise AI implementations.
• Experience with Vertex AI and Google Cloud generative AI capabilities.
• Experience with enterprise data governance, metadata, catalog and lineage platforms.
• Experience with Infrastructure as Code, CI/CD and modern cloud engineering practices.
• Experience defining enterprise architecture standards and technology reference architectures.
• Experience working directly with C-suite and senior technology leadership in complex transformation programs.

Location & Way of Working:

This is a client-facing leadership position based in Charlotte, United States. The candidate must be willing and able to work from the client location a minimum of three days per week.