Principal Data Architect

Job Description

Job Description

Job 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