Director Unstructured Presales Storage Leader
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About this role
Employer-provided description, formatted for easier reading.
Director Unstructured Presales Storage Leader
This role has been designed as 'Hybrid' with a requirement that you will work on average 2 days per week from an HPE office.
Who We Are
Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work. We help companies connect, protect, analyze, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today’s complex world. Our culture thrives on finding new and better ways to accelerate what’s next.
We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good.
If you are looking to stretch and grow your career our culture will embrace you. Open up opportunities with HPE.
Job Description
We are seeking a strategic and technically grounded Pre-Sales Leader to drive the next phase of growth for our X10K AI data platform business. This leader will own the pre-sales strategy, execution, and enablement required to accelerate customer adoption of AI Factory solutions across RAG, inference, and model training workloads.
The role combines customer engagement, technical leadership, sales execution, team development, and cross-functional influence. You will partner closely with enterprise customers, account teams, solution architects, partners, and Product Management to shape high-value opportunities, develop compelling solution architectures, and build repeatable go-to-market motions.
A data-first perspective is essential. You will guide customers in understanding how data is created, moved, enriched, accessed, and consumed across AI pipelines, positioning infrastructure and data platforms as enablers of business value rather than as the starting point of the conversation.
The ideal candidate brings strong commercial judgment, deep understanding of AI and data architectures, and the ability to lead teams through complex enterprise sales cycles across APAC and EMEA. You will help identify the right opportunities at the right time, align solutions to customer readiness and workload requirements, and ensure we win where we can deliver sustainable, measurable impact.
This is not a pure storage role. However, a strong understanding of how modern data platforms and storage technologies enable AI pipelines is essential.
Key Responsibilities
Pre-Sales Strategy and Leadership
- Define and execute the pre-sales strategy for the X10K AI data platform business.
- Partner with sales leadership to develop account strategies, territory plans, pipeline priorities, and opportunity qualification criteria.
- Establish repeatable approaches for identifying, shaping, qualifying, and advancing AI Factory opportunities.
- Apply strong technical and commercial judgment to prioritize opportunities based on customer readiness, workload fit, scale, competitive position, and likelihood of long-term success.
- Drive consistency in pre-sales execution, forecasting, deal inspection, technical validation, and executive engagement.
- Establish and track measures such as pipeline contribution, deal velocity, win rate, solution adoption, and customer outcomes.
Customer Engagement and Deal Leadership
- Lead strategic customer engagements with CTOs, CIOs, Heads of AI, Data Engineering leaders, and other executive stakeholders.
- Direct technical discovery sessions to understand business objectives, data characteristics, AI workloads, existing environments, and desired outcomes.
- Translate customer requirements into scalable AI architectures and compelling business-aligned solution designs.
- Guide pre-sales teams through complex, multi-stakeholder enterprise sales cycles.
- Serve as an executive-level technical advisor throughout the opportunity lifecycle.
- Align technical solutions to measurable business outcomes, including performance, cost efficiency, reliability, scalability, and time to value.
- Help sales teams position solutions with precision and credibility, ensuring that proposed architectures are fit for purpose.
Data-Centric AI Architecture
- Lead architecture discussions based on data requirements and lifecycle considerations, including:
- Data volume, velocity, variety, and distribution
- Structured and unstructured data
- Data locality, gravity, and movement patterns
- Data access, governance, metadata, and retrieval requirements
- Ensure solution designs optimize:
- Sequential versus random access patterns
- Batch versus real-time processing
- Data movement between storage, compute, and model layers
- Metadata, indexing, and retrieval efficiency for RAG
- Performance, cost, reliability, and trustworthiness
- Evaluate how data architecture decisions affect:
- Model performance and accuracy
- Latency and time-to-first-token
- GPU utilization
- Data ingestion and preparation requirements
- Overall operating cost and scalability
AI Solution Architecture, Sizing, and Validation
- Lead the development and validation of AI Factory architectures for:
- Retrieval-Augmented Generation
- Model inference and deployment
- Model training and fine-tuning
- Guide teams in sizing environments based on:
- GPU counts and configurations
- Data volumes and throughput requirements
- Model types and workload characteristics
- Ingestion, retrieval, and serving requirements
- Define performance expectations across the full AI pipeline.
- Establish methodologies for evaluating time-to-first-token, throughput, GPU efficiency, data access performance, and cost.
- Ensure proposed designs are technically sound, commercially viable, and aligned with customer requirements.
- Support proof-of-concept, benchmark, and technical validation activities where required.
X10K Data Platform Positioning
- Define and articulate the strategic value of X10K within modern AI architectures.
- Position data platforms as critical enablers of AI performance, scalability, reliability, and operational efficiency.
- Guide teams in positioning:
- Object storage and S3-based architectures
- Data pipelines and pipeline simplification or elimination strategies
- Vector databases and retrieval architectures
- AI frameworks and model-serving environments
- Hybrid and cloud-integrated AI deployments
- Align messaging and solution positioning to customer-specific data scale, access patterns, performance requirements, and business priorities.
- Help differentiate X10K through value-based conversations rather than infrastructure-only discussions.
Field Enablement and Team Development
- Build the capabilities of pre-sales teams through coaching, mentoring, structured deal reviews, and technical enablement.
- Create repeatable tools and assets, including:
- Discovery frameworks
- Qualification criteria
- Reference architectures
- Solution blueprints
- Sizing methodologies
- Competitive positioning
- Business-value models
- Enable account teams to confidently position AI solutions with both technical and executive audiences.
- Develop and deliver internal training, workshops, technical briefings, and field readiness programs.
- Promote consistent practices for customer discovery, architecture development, technical validation, and executive communication.
Cross-Functional Leadership
- Partner closely with Product Management to:
- Influence roadmap priorities across RAG, inference, and training use cases
- Provide structured feedback on customer requirements and solution gaps
- Identify competitive trends and market opportunities
- Improve product-market fit and field readiness
- Collaborate with Engineering, Marketing, Professional Services, Partners, and Customer Success to improve the complete customer experience.
- Create and present high-impact technical content, including reference architectures, design patterns, whitepapers, conference presentations, and internal or external publications.
- Represent the company an