Rackspace Technology
Solution Director, Analytics & AI
Rackspace is seeking a highly accomplished Solution Director, Analytics & AI to lead the design and sales of two critical solution portfolios: Generative AI/LLM solutions and Data modernization/Lakehouse architectures on AWS. This pivotal role requires mastery of both domains – leveraging generative AI capabilities (Amazon Q, Amazon Bedrock, QuickSight) to drive executive conversations and opportunity creation, while delivering enterprise data modernization through Lakehouse architectures using AWS native services (Glue, SageMaker Unified Studio) and leading platforms (Databricks on AWS, Snowflake on AWS). The position demands cross‑functional experience with proven ability to engage C‑level stakeholders, drive top‑of‑funnel opportunity creation, and maintain comprehensive account ownership across the entire customer lifecycle.
Responsibilities Strategic Leadership & Opportunity Development
Drive top‑of‑funnel opportunity creation through two parallel tracks: engaging C‑level stakeholders with generative AI demonstrations (Amazon Q, Amazon Bedrock) and identifying data modernization needs for Lakehouse transformations
Lead the design and architecture of dual solution portfolios:
Generative AI Solutions: Amazon Bedrock implementations, Amazon Q deployments, QuickSight with Q capabilities, RAG architectures, and custom LLM solutions
Data Modernization: Enterprise Lakehouse architectures using AWS Glue, SageMaker Unified Studio, Databricks on AWS, and Snowflake on AWS
Act as the trusted advisor positioning generative AI as the transformational vision while grounding delivery in robust data platform modernization
Develop compelling business cases that connect AI aspirations with practical data foundation requirements, demonstrating ROI across both portfolios
Stay current with advancements in generative AI (foundation models, LLMs) and modern data architectures (Lakehouse patterns, data mesh, unified analytics)
Contribute to Rackspace intellectual property through reference architectures covering both generative AI implementations and Lakehouse design patterns
Customer Engagement & Solution Delivery
Serve as the primary technical executive orchestrating both generative AI discussions and data modernization programs for strategic accounts
Build strategic relationships using two engagement models:
Executive Level: Amazon Q demonstrations, QuickSight analytics with generative BI, art‑of‑the‑possible sessions
Technical Level: Lakehouse architecture workshops, platform assessments (Databricks vs Snowflake vs AWS‑native), migration planning
Lead comprehensive consultative engagements that begin with generative AI vision (Amazon Q, Bedrock) and translate into concrete data modernization roadmaps
Develop Statements of Work (SOWs) that balance innovative AI capabilities with foundational data platform requirements
Guide customers through parallel journeys: generative AI adoption (POCs to production) and data platform modernization (legacy to Lakehouse)
Collaborate with sales teams positioning both solution portfolios strategically based on customer maturity and needs
Technical Excellence & Market Awareness
Maintain deep expertise across both solution domains:
Generative AI: Amazon Bedrock, Amazon Q, QuickSight Q, SageMaker JumpStart, prompt engineering, RAG architectures, vector databases
Data Platforms: AWS Glue, SageMaker Unified Studio, Databricks on AWS, Snowflake on AWS, Redshift, EMR, Apache Iceberg, Delta Lake
Demonstrate comprehensive understanding of how generative AI solutions depend on modern data foundations
Position AWS solutions effectively against other cloud platforms’ offerings in both generative AI (Azure OpenAI, Vertex AI) and data platforms (Azure Synapse, BigQuery)
Guide architectural decisions on build vs. buy for both AI capabilities and data platform components
Qualifications Required Experience
Dual Expertise Required:
Deep experience with generative AI technologies: Amazon Bedrock, Amazon Q, LLM architectures, RAG implementations
Proven track record delivering data modernization: Lakehouse architectures, Databricks and/or Snowflake implementations, AWS Glue/EMR deployments
At least 5 years as a senior‑level architect or solutions leader with hands‑on experience in both AI/ML and data platform modernization
Demonstrated success engaging C‑level executives using generative AI demonstrations while delivering complex data platform transformations
Strong understanding across the full spectrum:
AI/ML: Generative AI, foundation models, LLMs, traditional ML, prompt engineering, fine‑tuning
Data Platforms: Lakehouse architectures, data mesh, ETL/ELT, streaming, data governance, data quality
Proficiency in Python, SQL, and Spark with hands‑on experience in:
Generative AI: LangChain, vector databases, embedding models
Data Engineering: PySpark, Apache Iceberg/Delta Lake, orchestration tools
Proven ability to articulate both visionary AI possibilities and practical data platform requirements to diverse audiences
Preferred Qualifications
Experience with AWS professional services or AWS partner ecosystem across both AI and data domains
Hands‑on experience with:
Multiple Lakehouse platforms: Databricks, Snowflake, AWS‑native (Glue + Athena + Redshift)
Multiple AI platforms: AWS Bedrock, Azure OpenAI, Google Vertex AI
Industry certifications:
AWS: Solutions Architect Professional, Machine Learning Specialty, Data Analytics Specialty
Platform specific: Databricks Certified, Snowflake SnowPro
Experience with regulated industries requiring governance for both AI and data platforms
Track record building practices that deliver both generative AI solutions and data modernization programs
Published thought leadership in generative AI applications and/or modern data architectures
Educational Requirements
Bachelor’s degree in computer science, Data Science, Engineering, Mathematics, or related technical field
Advanced degree (Master’s or PhD) in a relevant field is highly preferred
What We Offer
Opportunity to lead dual portfolios at the intersection of generative AI innovation and data platform modernization
Direct engagement with C‑level executives on transformational initiatives
Access to latest AWS technologies across AI and data services
Work with leading platforms: Databricks, Snowflake, AWS native services
Comprehensive professional development and certification support
Competitive compensation and benefits package
Travel
As per business requirements
This role requires occasional travel (up to 25%) to customer sites and AWS events
Sponsorship
This role is not sponsorship eligible.
Candidates need to be legally allowed to work in the US for any employer
The ideal candidate will excel at both selling the vision of generative AI transformation and delivering the reality of enterprise data modernization, combining deep technical expertise with exceptional business acumen and executive presence.
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Rackspace is seeking a highly accomplished Solution Director, Analytics & AI to lead the design and sales of two critical solution portfolios: Generative AI/LLM solutions and Data modernization/Lakehouse architectures on AWS. This pivotal role requires mastery of both domains – leveraging generative AI capabilities (Amazon Q, Amazon Bedrock, QuickSight) to drive executive conversations and opportunity creation, while delivering enterprise data modernization through Lakehouse architectures using AWS native services (Glue, SageMaker Unified Studio) and leading platforms (Databricks on AWS, Snowflake on AWS). The position demands cross‑functional experience with proven ability to engage C‑level stakeholders, drive top‑of‑funnel opportunity creation, and maintain comprehensive account ownership across the entire customer lifecycle.
Responsibilities Strategic Leadership & Opportunity Development
Drive top‑of‑funnel opportunity creation through two parallel tracks: engaging C‑level stakeholders with generative AI demonstrations (Amazon Q, Amazon Bedrock) and identifying data modernization needs for Lakehouse transformations
Lead the design and architecture of dual solution portfolios:
Generative AI Solutions: Amazon Bedrock implementations, Amazon Q deployments, QuickSight with Q capabilities, RAG architectures, and custom LLM solutions
Data Modernization: Enterprise Lakehouse architectures using AWS Glue, SageMaker Unified Studio, Databricks on AWS, and Snowflake on AWS
Act as the trusted advisor positioning generative AI as the transformational vision while grounding delivery in robust data platform modernization
Develop compelling business cases that connect AI aspirations with practical data foundation requirements, demonstrating ROI across both portfolios
Stay current with advancements in generative AI (foundation models, LLMs) and modern data architectures (Lakehouse patterns, data mesh, unified analytics)
Contribute to Rackspace intellectual property through reference architectures covering both generative AI implementations and Lakehouse design patterns
Customer Engagement & Solution Delivery
Serve as the primary technical executive orchestrating both generative AI discussions and data modernization programs for strategic accounts
Build strategic relationships using two engagement models:
Executive Level: Amazon Q demonstrations, QuickSight analytics with generative BI, art‑of‑the‑possible sessions
Technical Level: Lakehouse architecture workshops, platform assessments (Databricks vs Snowflake vs AWS‑native), migration planning
Lead comprehensive consultative engagements that begin with generative AI vision (Amazon Q, Bedrock) and translate into concrete data modernization roadmaps
Develop Statements of Work (SOWs) that balance innovative AI capabilities with foundational data platform requirements
Guide customers through parallel journeys: generative AI adoption (POCs to production) and data platform modernization (legacy to Lakehouse)
Collaborate with sales teams positioning both solution portfolios strategically based on customer maturity and needs
Technical Excellence & Market Awareness
Maintain deep expertise across both solution domains:
Generative AI: Amazon Bedrock, Amazon Q, QuickSight Q, SageMaker JumpStart, prompt engineering, RAG architectures, vector databases
Data Platforms: AWS Glue, SageMaker Unified Studio, Databricks on AWS, Snowflake on AWS, Redshift, EMR, Apache Iceberg, Delta Lake
Demonstrate comprehensive understanding of how generative AI solutions depend on modern data foundations
Position AWS solutions effectively against other cloud platforms’ offerings in both generative AI (Azure OpenAI, Vertex AI) and data platforms (Azure Synapse, BigQuery)
Guide architectural decisions on build vs. buy for both AI capabilities and data platform components
Qualifications Required Experience
Dual Expertise Required:
Deep experience with generative AI technologies: Amazon Bedrock, Amazon Q, LLM architectures, RAG implementations
Proven track record delivering data modernization: Lakehouse architectures, Databricks and/or Snowflake implementations, AWS Glue/EMR deployments
At least 5 years as a senior‑level architect or solutions leader with hands‑on experience in both AI/ML and data platform modernization
Demonstrated success engaging C‑level executives using generative AI demonstrations while delivering complex data platform transformations
Strong understanding across the full spectrum:
AI/ML: Generative AI, foundation models, LLMs, traditional ML, prompt engineering, fine‑tuning
Data Platforms: Lakehouse architectures, data mesh, ETL/ELT, streaming, data governance, data quality
Proficiency in Python, SQL, and Spark with hands‑on experience in:
Generative AI: LangChain, vector databases, embedding models
Data Engineering: PySpark, Apache Iceberg/Delta Lake, orchestration tools
Proven ability to articulate both visionary AI possibilities and practical data platform requirements to diverse audiences
Preferred Qualifications
Experience with AWS professional services or AWS partner ecosystem across both AI and data domains
Hands‑on experience with:
Multiple Lakehouse platforms: Databricks, Snowflake, AWS‑native (Glue + Athena + Redshift)
Multiple AI platforms: AWS Bedrock, Azure OpenAI, Google Vertex AI
Industry certifications:
AWS: Solutions Architect Professional, Machine Learning Specialty, Data Analytics Specialty
Platform specific: Databricks Certified, Snowflake SnowPro
Experience with regulated industries requiring governance for both AI and data platforms
Track record building practices that deliver both generative AI solutions and data modernization programs
Published thought leadership in generative AI applications and/or modern data architectures
Educational Requirements
Bachelor’s degree in computer science, Data Science, Engineering, Mathematics, or related technical field
Advanced degree (Master’s or PhD) in a relevant field is highly preferred
What We Offer
Opportunity to lead dual portfolios at the intersection of generative AI innovation and data platform modernization
Direct engagement with C‑level executives on transformational initiatives
Access to latest AWS technologies across AI and data services
Work with leading platforms: Databricks, Snowflake, AWS native services
Comprehensive professional development and certification support
Competitive compensation and benefits package
Travel
As per business requirements
This role requires occasional travel (up to 25%) to customer sites and AWS events
Sponsorship
This role is not sponsorship eligible.
Candidates need to be legally allowed to work in the US for any employer
The ideal candidate will excel at both selling the vision of generative AI transformation and delivering the reality of enterprise data modernization, combining deep technical expertise with exceptional business acumen and executive presence.
#J-18808-Ljbffr