Qualifications

Minimum 10 years of progressive experience in data architecture, data engineering, cloud architecture, or related technical disciplines
Minimum 7 years of hands-on experience designing, deploying, and operating data and AI workloads on AWS in enterprise environments
Bachelor's degree in Computer Science, Information Systems, Engineering, or equivalent practical experience
Demonstrated real-world experience designing and delivering enterprise data platforms, including data lakes, data warehouses, streaming architectures, and data governance frameworks
Deep expertise in AWS data and AI platform services: Redshift, Glue, Lake Formation, Kinesis, EMR, Athena, SageMaker, Bedrock, and related ecosystem services
Proven hands-on experience with Databricks or Snowflake deployed on AWS in enterprise environments
Demonstrated experience designing AI-ready data architectures, including data preparation pipelines, vector stores, retrieval-augmented generation (RAG) patterns, and AI governance frameworks
Proven ability to develop and articulate AI & Data business cases, connecting technical architecture decisions to client outcomes and measurable business value
Experience authoring SOWs, LOEs, reference architectures, and delivery playbooks that technical teams can execute from
Strong communication skills, able to present data and AI architecture concepts to executive audiences and technical depth to delivery teams with equal effectiveness

Strongly Desired Skills

AWS Certifications

AWS Certified Solutions Architect: Professional (strongly preferred)
AWS Certified Data Engineer: Associate or Professional
AWS Certified Machine Learning: Specialty

Platform Certifications

Databricks Certified Data Engineer (Associate or Professional)
Snowflake SnowPro Core or Advanced: Data Engineer

Technical Depth

Enterprise data architecture patterns: data lakehouse, data mesh, data fabric, medallion architecture
Data pipeline and orchestration tooling: Apache Airflow, AWS Step Functions, Glue workflows, dbt
Streaming and real-time data architectures: Kinesis Data Streams, Kafka on AWS (MSK), EventBridge
Data governance and cataloging: AWS Lake Formation, Glue Data Catalog, data lineage and quality frameworks
AI operationalization on AWS: Bedrock, RAG architecture design, agentic workflow patterns, prompt engineering, AI governance
Vector database and embedding patterns relevant to enterprise AI workloads
SageMaker for model deployment, inference, and MLOps; experience with model training is a differentiator
Application security patterns relevant to data and AI (data access controls, PII handling, model governance)
Cloud cost modeling and FinOps principles as applied to data platform architecture decisions

Multi-Cloud Exposure

Familiarity with Azure data and AI services (Azure Synapse, Azure Data Factory, Azure AI Foundry, Microsoft Fabric)
Experience advising clients on multi-cloud data strategy or workload placement decisions across AWS and Azure
Microsoft Azure certifications (any level) are a plus

What Success Looks Like in Year One

WWT's AI & Data service offerings are sharper, more repeatable, and faster to scope, measurably reducing presales cycle time
Solution Architects trust this role as their go-to technical escalation for AI & Data pursuits
Delivery oversight has prevented at least one significant architectural problem from reaching the client
The frameworks, playbooks, and standards this role has built have made WWT's AI & Data delivery more scalable, enabling the team to take on greater volume without sacrificing quality
Client satisfaction scores on AI & Data engagements reflect a measurable improvement in delivery consistency and outcome achievement

Want to learn more about Consulting Services? Check us out on our platform:

https://www.wwt.com/consulting-services

Certain states and localities require employers to post a reasonable estimate of salary range. A reasonable estimate of the current base pay range for this position is $140,000 to $175,000 annually. Actual salary will be based on a variety of factors, including shift, location, experience, skill set, performance, licensure and certification, and business needs. The range for this position in other geographic locations may differ. Certain positions may also be eligible for variable incentive compensation, such as bonuses or commissions, that is not included in the base pay.

The well-being of WWT employees is essential. So, when it comes to our benefits package, WWT has

Lead Cloud Solution Architect- AI & Data (AWS) at World Wide Technology