Pre-Sales Solution Architect

Job Location: Pune
Location Flexibility: Multiple Locations in Country
Req Id: 12013
Posting Start Date: 9/8/26
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.
Relocation Supported: Yes
Visa Sponsorship Approved: No

Work arrangement
No