Enterprise Data & AI Architecture Review Session
This session is designed for experienced data professionals, architects, engineering leads, startups, and platform teams looking for practical guidance on designing, scaling, optimizing, or modernizing enterprise-grade data and AI systems.
Over the last 15+ years, I’ve worked across enterprise data engineering, analytics architecture, Spark ecosystems, AWS, Databricks platforms, marketing analytics systems, and modern AI-enabled data architectures. I currently lead large-scale data engineering initiatives involving distributed data platforms, governance strategy, production engineering, and AI/data modernization efforts.
This is NOT a beginner mentoring or debugging session.
The focus is on real-world enterprise architecture decisions, scalability tradeoffs, platform strategy, production reliability, governance, and long-term system evolution.
In this architecture review session, we can discuss:
• Enterprise Databricks architecture strategy
• Lakehouse and medallion architecture design
• Spark performance optimization
• Distributed data platform scalability
• Governance and security strategy
• Data platform modernization roadmaps
• Batch vs streaming tradeoffs
• Production pipeline architecture
• Cost optimization and operational efficiency
• AI-ready data platform design
• RAG and vector architecture considerations
• AI + analytics integration strategy
• System design and platform evolution planning
• Team structure and platform ownership models
Ideal for:
• Senior Data Engineers
• Data Architects
• Analytics Engineers
• Engineering Leads
• Platform Teams
• Startups building data platforms
• Teams adopting Databricks or AI systems
You’ll leave the session with:
• Architecture feedback and recommendations
• Scalability and optimization insights
• Enterprise-focused design guidance
• Identification of technical risks and bottlenecks
• Clear next-step recommendations
• Practical tradeoff analysis based on your use case
Please share your architecture diagrams, current stack, goals, or problem statement before the session so the discussion can be highly tailored and actionable.