Presales Solutions Architect Data Architect

What success looks like in this role:

We are seeking an experienced Presales Data Architect 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

You will be successful in this role if you have:

BA/BS degree and 8 years relevant experience OR equivalent combination of education and experience
Masters degree preferred.

Required Qualifications

Experience

• 812 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:
  • Designing enterprise data solutions
  • Supporting and influencing large deals
  • Building cost models and ROI justifications
• Experience in RFP responses and technical proposal development

Technical Expertise

• Strong experience with enterprise database platforms:
  • Oracle SQL Server DB2 Teradata or similar
• Deep understanding of:
  • Data warehousing concepts (EDW ODS data marts)
  • Data modeling techniques (dimensional normalized data vault)
• Hands-on experience with:
  • ETL/ELT tools (Informatica DataStage SSIS etc.)
  • Batch and near real-time data integration
• Knowledge of:
  • Data governance metadata and data quality tools
  • Performance tuning and query optimization
• Experience with:
  • High availability backup and disaster recovery strategies
• Familiarity with BI/reporting tools (Power BI Tableau Cognos etc.)
• Proficiency in using GenAI tools for presales productivity and documentation

Business & Soft Skills

• Strong ability to translate data architecture into business value
• Excellent communication and presentation skills
• Consultative approach to discovery and problem-solving
• Ability to manage multiple presales opportunities
• Strong collaboration across cross-functional teams
• Financial acumen in cost modeling and ROI analysis

Certifications & Nice-to-Haves

• Certifications in database technologies or data management
• Knowledge of frameworks such as DAMA-DMBOK
• Experience in system integrators or managed services environments
• Familiarity with MPP or appliance-based data systems (e.g. Teradata Netezza)
• Experience with data modeling tools (ERwin ER/Studio etc.)
• Thought leadership in enterprise data architecture

Success Metrics

• Contribution to deal wins involving data solutions
• Accuracy and competitiveness of cost models
• Quality and scalability of architecture designs
• Customer satisfaction and successful delivery alignment
• Reusability of solution frameworks and assets

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