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Pre-Sales Solution Architect

FUJITSU LIMITED
Pune, IND
Full-time
Senior · 10+ years experience
Onsite
Discovered 1 months ago
Azure MLAWS SageMakerDatabricksPalantirTensorFlowScikit-learn

Full Job Posting

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.

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

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.

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