Principal Data Architect

Job DescriptionJob DescriptionJob title : AI Multi-Cloud Architecture Lead Job Location : Palm Bach Florida , (Onsite ) Role Summary Responsible for defining and advancing a cloud-agnostic, AI-enabled architecture strategy that supports enterprise analytics, automation, and operational decision-making across multi-cloud environments. This role leads architecture standards and governance across AWS and GCP while actively delivering hands-on prototypes, data pipelines, and AI integrations to accelerate adoption. Operating as a shared services architecture function, this role both guides and demonstrates best practices—bridging strategy and execution to ensure scalable, cost-efficient, and production-ready solutions aligned with ServiceNow CMDB/APM and Apptio models. Core Role Identity Dimension Expectation Architecture Defines standards, patterns, governance Delivery Builds POCs, pipelines, and AI integrations Model Shared service / enterprise enablement Authority Influences + demonstrates (not just advises) Cloud Multi-cloud, cloud-agnostic mindset Key Responsibilities 1. Multi-Cloud Architecture Governance Define and implement cloud-agnostic architecture patterns across AWS and GCP Standardize GCP governance aligned to AWS controls Establish reusable reference architectures for data, AI, and infrastructure Promote abstraction via: Containers (Kubernetes) APIs Infrastructure as Code (Terraform) 2. Hands-On Enablement (POCs Pipeline Delivery) Build proof-of-concept solutions to validate architecture patterns Develop and optimize data pipelines and integrations across systems (ServiceNow, Apptio, Jira) Implement AI-enabled workflows (model integration, automation) Provide hands-on support to delivery teams to accelerate adoption Translate architecture into working, scalable solutions 3. AI Integration MLOps Enablement Design and implement AI-ready pipelines (structured + unstructured data) Support: Model integration into enterprise workflows MLOps lifecycle enablement (CI/CD, monitoring, governance) AI tool/vendor evaluation Mature organization from: POCs → Embedded AI → Governed enterprise AI 4. Data Architecture Integration (CMDB/APM-Aligned) Architect data flows integrating: ServiceNow (CMDB/APM) Apptio (cost transparency) Jira (delivery data) Address key challenges: Data latency Data duplication Cost visibility gaps Enforce system-of-record and data ownership principles 5. Governance FinOps (Advisory + Enablement) Define standards for: Cloud cost optimization (FinOps) AI governance and lifecycle management Data quality and pipeline SLAs Support KPI transparency: Cloud cost per application Data pipeline reliability AI ROI Guide teams while enabling them through working solutions 6. Platform Strategy Shared Services Leadership Act as a central architecture leader and enabler Support teams through: Architecture reviews POC delivery Design guidance Build reusable enterprise assets: Patterns Templates Integration frameworks Required Experience 7+ years in cloud architecture, data engineering, or infrastructure Proven experience in multi-cloud environments (AWS + GCP) Demonstrated ability to: Design architecture and deliver working solutions Build data pipelines and integrations Strong experience with: Python, SQL ETL/ELT pipelines Infrastructure as Code (Terraform preferred) Containers (Kubernetes) AI Modern Architecture Requirements Hands-on experience with: AI/ML integration into enterprise pipelines MLOps or AI lifecycle tooling Experience evaluating and implementing: AI platforms Automation tooling Preferred Experience ServiceNow CMDB/APM integration Apptio (cost allocation / FinOps) Experience solving: Cross-system duplication Data lineage challenges Exposure to Generative AI integration Success Metrics (Aligned to Your KPIs) Reduction in cloud cost per application Improvement in pipeline SLAs Reduction in duplicate data/integrations Increase in production AI-enabled workflows Adoption of multi-cloud architecture standards Number of successful POCs transitioned to production AI Multi Cloud Architecture Lead to drive enterprise wide cloud, data, and AI transformation across AWS and GCP environments. This role combines strategic architecture leadership with hands on delivery, enabling organizations to modernize platforms, optimize cloud investments, and accelerate AI adoption.The role is responsible for defining cloud agnostic architecture standards, governance frameworks, and reusable reference architectures for data, AI, and infrastructure. A key responsibility is to design and deliver Proof of Concepts (POCs), The successful candidate will drive AI and MLOps adoption by building AI ready data architectures, integrating AI/ML models into enterprise workflows, and establishing governance, monitoring, and lifecycle management practices. The role also focuses on governance and FinOps, including cloud cost optimization, AI governance, data quality standards, and KPI driven performance management. Acting as a central architecture leader and shared services enabler,. Required Experience Skills 7+ years in Cloud Architecture, Data Engineering, or Infrastructure. Strong expertise in AWS and GCP multi cloud environments. Hands on experience with Python, SQL, ETL/ELT pipelines, Terraform, Kubernetes, APIs, and cloud native architectures. Experience implementing AI/ML solutions, MLOps practices, automation, and enterprise integrations. Ability to translate architecture strategies into scalable, secure, and production ready solutions. ServiceNow CMDB/APM integration. Data lineage, governance, and cross platform integration expertise