Solutions Director - Analytics & AI

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 and required experience:

• 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