Pre-Sales Solution Architect
Summary
Customer-facing pre-sales architect in Fujitsu's Global Presales & Solutions team (Pune/Noida/Bangalore, India), designing and pitching Data, Analytics, AI/ML and cloud data platform solutions for global customers. Day to day: discovery workshops, target-state architecture, RFP/RFI responses and pricing, across Azure, AWS, GCP, Databricks and Snowflake ecosystems.
Pre-Sales Solution Architect
Role Summary
The Pre-Sales Solution Architect for Data & AI is a customer-facing role responsible for shaping, designing, and positioning Data, Analytics, AI/ML, DataOps, and Cloud Data Platform solutions for global customers. The role works closely with Sales, Industry Leads, Delivery Teams, Technology Partners, and Global Solution Architects to qualify opportunities, develop solutions, respond to RFPs, and articulate the business value of Data & AI transformation.
Key Responsibilities
- Lead customer discovery, assessments, and solution workshops.
- Identify opportunities for Data Modernization, Data Migration, Analytics Transformation, AI Enablement, Data Governance, and Managed Services.
- Design end-to-end Data & AI solutions covering Data Platforms, Data Warehouses, Lakehouses, Data Integration, Analytics, AI/ML, Data Governance, DataOps, and MLOps.
- Develop target-state architectures, roadmaps, operating models, and implementation approaches.
- Lead solution development for RFPs, RFIs, proposals, and customer presentations.
- Create solution narratives, estimations, assumptions, risks, and transition strategies.
- Collaborate with delivery teams to develop commercially viable and technically feasible solutions.
- Support pricing models, resource plans, and service definitions.
Technical Expertise
Data Platforms & Engineering
- Data Lakes, Data Warehouses, Lakehouse Architectures
- Enterprise Data Platforms, Data Mesh, Data Fabric
- Data Integration, ETL/ELT
- Data Modelling and Master Data Management
Analytics & BI
- Enterprise Reporting
- Self-Service Analytics
- Operational Intelligence
- Dashboard Strategy
- KPI Frameworks
- Data Storytelling
AI/ML
- Machine Learning Lifecycle
- Predictive Analytics
- Data Science
- MLOps
- AI Governance and Responsible AI
- Generative AI and AI Agents
- Cognitive Services
DataOps & MLOps
- CI/CD for Data Pipelines
- Data Observability
- Pipeline Automation
- DevSecOps Integration
- Monitoring and Continuous Improvement
Cloud & Data Platforms
Microsoft Azure: Azure Data Factory, Synapse, Databricks, Data Lake, Microsoft Fabric, Azure ML, Purview
AWS: Glue, Redshift, S3
GCP: BigQuery, Dataflow, Vertex AI
Modern Data Platforms: Snowflake, Databricks, Cloudera, Palantir, Talend
Success Metrics
- Win Rate
- AI & Data Services Solution Quality
- Competitive Pricing
- Team Collaboration
- Customer Satisfaction
Skills
- Agentic AI
- AI
- Ai Enablement
- Analytics
- Automation
- AWS
- Azure
- Azure Data Factory
- BigQuery
- CI/CD
- Cloud
- Data Governance
- Data Lake
- Data Modeling
- Data Pipelines
- Data Science
- Databricks
- DevSecOps
- ELT
- ETL
- GCP
- Generative AI
- Lakehouse
- Machine Learning
- Master Data Management
- Microsoft Fabric
- MLOps
- Observability
- Pre-sales
- Predictive Analytics
- Redshift
- S3
- Snowflake
- Talend
- Vertex AI