Presales Solutions Architect - Data & AI

What success looks like in this role: We are seeking an experienced Presales Architect - Applications with deep expertise in enterprise data architecture and on-premises data platforms to support complex sales engagements. This role focuses on designing scalable, secure, and high-performance data solutions within traditional enterprise environments , enabling organizations to modernize legacy systems, improve data governance, and enhance analytics capabilities. You will partner with sales, delivery, and customer stakeholders to shape data strategies, build compelling proposals, and deliver measurable business outcomes. Key Responsibilities 1. Solution Architecture & Design Lead discovery sessions to assess existing enterprise data ecosystems , legacy platforms, and business requirements Design end-to-end data architectures including: Enterprise Data Warehouses (EDW) Operational Data Stores (ODS) Data marts and reporting systems Batch and near real-time data integration Define architectures across: Relational databases (Oracle, SQL Server, DB2, Teradata, etc.) ETL and data integration tools Storage, archival, and lifecycle management systems Develop logical and physical data models aligned to business needs Ensure architectures meet requirements for performance, scalability, high availability, and disaster recovery Present solutions to technical and business stakeholders, linking architecture to business value 2. Cost Modeling & Financial Analysis Develop detailed cost models covering: Hardware (servers, storage, networking) Software licensing (databases, ETL, BI tools) Data processing and integration workloads Ongoing operations and support Build bottom-up estimates using: Data volumes and growth projections Workload and performance requirements Conduct ROI and TCO analysis for: Legacy modernization initiatives Platform consolidation and optimization Compare alternative architecture approaches (e.g., centralized vs distributed data platforms) Validate cost assumptions with delivery and finance teams 3. Technical Proposal Development Develop comprehensive proposals including: Data architecture diagrams Data flow and integration patterns Implementation roadmaps and migration plans Resource planning and timelines Risk mitigation strategies and governance models Clearly articulate business value including: Improved data quality and reliability Faster reporting and analytics Operational efficiency and cost savings Collaborate with delivery, engineering, and finance teams to ensure feasibility Use GenAI tools (e.g., Microsoft Copilot, ChatGPT) to: Accelerate documentation Improve clarity and structure of proposals Synthesize complex requirements 4. Data Modernization & Transformation Define strategies for: Legacy data warehouse modernization Data platform consolidation ETL optimization and re-engineering Data archival and lifecycle management …