Pre-Sales Solution Architect – Data Services, Analytics, AI/ML & Data Platforms
Role Summary
The Pre-Sales Solution Architect for Data & AI is a strategic and customer-facing role within the Global Pre-Sales & Solutions (GPS) organization responsible for shaping, designing, and positioning business-led Data, Analytics, AI/ML, DataOps, and Cloud Data Platform solutions for global customers.
The role works closely with Sales, Industry Leads, Delivery Teams, UVANCE Digital Shifts, Technology Partners, and Global Solution Architects to qualify opportunities, define solution, design delivery roadmaps, respond to RFPs, create differentiated solutions, and articulate the business value of Data & AI-led transformation.
Key Responsibilities
- Opportunity Qualification
- Engage with region / customers to understand business objectives, challenges, data maturity, and transformation priorities.
- Lead discovery workshops, assessments, and discussions across business and technology stakeholders.
- Identify opportunities for Data Modernization, Data Migration, Analytics Transformation, AI Enablement, Data Governance, and Managed Services.
- Solution Architecture & Design
- Design end-to-end Data & AI solutions encompassing:
- Data Platforms
- Modern Data Warehouses
- Data Lakes and Lakehouse Architectures
- Data Integration & Engineering
- Business Intelligence & Analytics
- AI/ML Platforms
- Data Governance & Data Quality
- DataOps & MLOps
- Define target-state architectures, operating models, roadmaps, and implementation approaches.
- Create scalable, secure, resilient, and cost-effective cloud-native architectures.
- RFP Response & Solution Development
- Lead solution development for RFPs, RFIs, proposals, and customer presentations.
- Develop solution narratives, effort estimations, assumptions, risks, and transition strategies.
- Collaborate with delivery teams to create commercially viable and technically feasible solutions.
- Contribute to pricing models, resource plans, and service definitions.
Core Technical Expertise
Data Platform & Data Engineering
- Data Lakes
- Data Warehouse
- Lakehouse Architectures
- Enterprise Data Platforms
- Data Mesh
- Data Fabric
- Data Integration
- ETL / ELT Design
- Data Modelling
- Master Data Management
Analytics & Business Intelligence
- Enterprise Reporting
- Self-Service Analytics
- Operational Intelligence
- Dashboard Strategy
- KPI Frameworks
- Data Storytelling
Artificial Intelligence & Machine Learning
- Machine Learning Lifecycle
- Predictive Analytics
- Data Science
- MLOps
- AI Governance
- Responsible AI
- Generative AI
- AI Agents
- Cognitive Services
Platforms and ecosystems include Azure ML, AWS SageMaker, Databricks, Palantir, TensorFlow and Scikit-learn capabilities reflected in current Fujitsu Data & AI solutions.
DataOps & MLOps
- CI/CD for Data Pipelines
- Data Observability
- Data Reliability Engineering
- Pipeline Automation
- DevSecOps Integration
- Monitoring & Continuous Improvement
Cloud Data Platforms
Microsoft Azure
- Azure Data Factory
- Azure Synapse
- Azure Databricks
- Azure Data Lake
- Microsoft Fabric
- Azure Machine Learning
- Azure Purview
AWS
- AWS Glue
- Redshift
- S3
GCP
- BigQuery
- Dataflow
- Vertex AI
Modern Data Platforms
- Snowflake
- Databricks
- Cloudera
- Palantir
- Talend
Success Metrics
- Win Rate
- AI & Data Services Solutions Quality in deals
- Competitive Pricing
- Team Player
- Customer Satisfaction
Required Experience
- 10+ years of experience in Data Services, Data Engineering, Analytics, Data Integration, Data Platforms, or Data Architecture.
- 5+ years in customer-facing consulting, pre-sales, solutioning, or architecture roles.
- Proven experience shaping and winning large-scale Data & Analytics transformation engagements.
- Experience designing enterprise-wide Data Platforms and Data Modernization programs.
- Experience developing business cases, proposals, and strategic transformation roadmaps.
Preferred Skills & Advisory Experience
Experience across enterprise data strategy, governance, AI adoption roadmaps, generative AI use cases, data monetization, self-service analytics, and data product operating models.
Good to Have Skills
- Data quality, metadata management, lineage, compliance, regulatory controls, and information security.
- Ability to position DataOps, AIOps, automation, and managed services for continuous improvement.
Collaboration & Stakeholder Management
- Partner with regional sales, service lines, partner ecosystems, and delivery teams to shape differentiated solutions.
- Contribute to deal assurance, pursuit strategy, governance, and solution quality improvement.