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
Recommend approaches such as:
Replatforming to modern database systems
Data model redesign (dimensional, normalized, data vault)
Enable capabilities for:
Near real-time data availability
Improved reporting performance
Better data accessibility
5. Data Governance, Quality & Security
Design and implement frameworks for:
Data governance and stewardship
Data quality management
Metadata management and lineage
Ensure compliance with enterprise security standards:
Role-based access control
Data masking and encryption
Regulatory compliance requirements
6. Delivery Partnership & Post-Sales Alignment
Ensure solutions are aligned with delivery capabilities and operational models
Develop transition documents and implementation playbooks
Support project kickoffs and early delivery alignment
Define SLAs, KPIs, and operational metrics
Capture feedback to improve solution quality and reusability
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You will be successful in this role if you have:
BA/BS degree and 10+ years’ relevant experience OR equivalent combination of education and experience
Master’s degree preferred.
Experience
8–12 years of experience in data architecture, data engineering, or presales consulting
Minimum 5 years focused on enterprise data platforms in on-premises environments
Proven track record in: