Director Presales Enterprise Architect (DataAI and Applications)

We are seeking expressions of interest from experienced Presales Enterprise Architect (Data/AI and Applications). This senior presale enterprise architecture role is central to NTT DATA Australias integrated full-stack go-to-market strategy. The purpose of the role is to design position and win data AI applications modernization and managed services deals by bringing together

• Deep solution design expertise across enterprise data platforms analytics data governance AI / ML GenAI and responsible AI operating models.
• Strong architectural skills across modern applications COTS / packaged applications SaaS ecosystems API-led integration cloud-native modernization and application managed services.
• Modern AIOps automation agentic AI and GenAI capabilities that enhance reliability productivity business-process efficiency and decision support.
• Cross-tower architectures spanning applications data cloud infrastructure security workplace and managed services.

The Presales EA acts as the trusted architecture leader for strategic pursuits translating client business needs into executable architectures that leverage NTT DATA capabilities and partner ecosystems. Depending on candidate strengths and client demand the role may emphasize Data/AI Applications or an integrated Data/AI plus Applications transformation agenda.

Primary Duties

1. Account Planning

• Establish the enterprise architecture view of priority accounts including:
• Current data estate analytics platforms AI maturity data governance posture reporting landscape data quality challenges and modernization imperatives.
• Application estate COTS / SaaS footprint integration patterns cloud adoption posture DevSecOps maturity and application support model.
• Business pain points and value pools where data AI GenAI automation AIOps and application modernization can materially improve outcomes.
• Identify multi-tower plays across data platforms AI application modernization cloud integration security workplace and managed services.
• Shape portfolio motions with hyperscalers data platform vendors SaaS providers AI ecosystem partners and industry solution partners.
• Support client managers with account hypotheses opportunity themes win themes and line-of-sight bookings linked to data AI and applications value.

2. Opportunity Seeding

• Lead C-suite line-of-business and technology workshops to shape opportunities leveraging:
• Enterprise data platforms lakehouse data mesh / fabric data integration streaming master data data quality and data governance capabilities.
• AI / ML GenAI retrieval-augmented generation agentic AI responsible AI model operations and AI adoption roadmaps.
• Application modernization COTS rationalization SaaS adoption ERP / CRM / ITSM modernization API-led integration workflow platforms and cloud-native engineering.
• AIOps-driven observability predictive operations automated remediation and AI-assisted application support.
• Create KPI-linked outcome narratives for large deals including revenue growth customer experience productivity decision quality risk reduction data trust and cost optimization.
• Build compelling demonstrations reference architectures and use-case roadmaps that connect client pains and ambitions to NTT DATA offerings.

3. Opportunity Strategy & Execution

• Serve as the data AI and applications deal architect across analytics AI cloud applications integration infrastructure security workplace and managed services.
• Design integrated solutions that combine:
• Data platform architectures governance models data quality controls metadata / lineage privacy-by-design and data product operating models.
• AI architectures covering ML GenAI RAG agentic AI Responsible AI MLOps / LLMOps model monitoring and value realization.
• Modern application architectures COTS / SaaS modernization API / event-driven integration microservices DevSecOps and application managed services.
• Cloud-native architectures across AWS Azure Google Cloud SaaS and hybrid environments.
• AIOps observability automation and GenAI copilots to improve reliability reduce MTTR and accelerate operational productivity.
• Work with technical architects to ensure the solution is scalable secure commercially competitive delivery-assured and aligned to client governance and risk requirements.
• Shape TCO pricing structures value levers benefit cases consumption models and risk-reward constructs.

4. Deal / Bid Leadership

• Lead the architecture workstream for major RFPs RFIs proposals and proactive pursuits involving data AI applications cloud and operations transformation.
• Define architecture content including:
• Target-state architecture reference models migration patterns transition roadmap and implementation phasing.
• Data operating model AI governance model application support model AIOps model DevSecOps model and service governance structure.
• Integration architecture across data applications AI services workflow platforms security and operations tooling.
• Value case solution assumptions risks dependencies transition approach and differentiation narrative.
• Partner with bid finance commercial legal risk delivery and alliance teams to optimize solution quality price competitiveness and deal assurance.
• Present architecture value and transformation approach to client executives technical leadership and internal governance forums.

5. Portfolio Ideation

• Provide thought leadership to evolve NTT DATA Data/AI and Applications offerings reference architectures accelerators and packaged value propositions.
• Inform portfolio enhancements by integrating:
• Modern data architectures including lakehouse data mesh / fabric analytics engineering semantic layers and industry data models.
• AI and GenAI patterns including RAG agentic workflows AI copilots MLOps / LLMOps responsible AI and AI adoption playbooks.
• Modern application architectures including microservices API-led integration event-driven architectures cloud-native platforms DevSecOps and AIOps-enabled support.
• Process intelligence and automation capabilities for business operations and managed services.
• Benchmark competitor offerings and partner propositions to keep NTT DATA positioned on the leading edge of practical value-producing technology trends.
• Develop reusable pursuit assets architecture patterns industry narratives calculators and demonstration storylines.

6. In-Contract Innovation Strategy

• Drive innovation across installed accounts by defining roadmaps for data modernization AI use-case scaling GenAI adoption application rationalization and cloud optimization.
• Identify opportunities to reduce incidents and MTTR through AIOps improve developer productivity through AI-assisted engineering and improve business processes through agentic AI and automation.
• Define measurable innovation outcomes linked to contractual commitments service levels cost-to-serve customer experience data quality and business KPIs.
• Work with delivery and account teams to keep transformation roadmaps current across transition modernization and steady-state operations.

7. Governance & Thought Leadership

• Provide architecture assurance during complex pursuits and in-contract transformation programs.
• Represent NTT DATA at client and industry forums on data modernization AI adoption GenAI application modernization cloud-native engineering AIOps and agentic AI.
• Coach account teams and solution architects on executive storytelling value engineering architecture trade-offs and outcome-led deal shaping.

Key Deliverables

• Enterprise data AI application COTS SaaS integration and cloud landscape assessments.
• Target-state architectures covering data platforms AI / ML GenAI applications integration cloud security AIOps and operations layers.
• Data / AI maturity assessments application portfolio assessments modernization roadmaps migration approaches and operating model recommendations.
• Value propositions and business cases quantifying improvements in revenue cost quality risk customer experience employee productivity and operational outcomes.
• Solution blueprints transition and transformation approaches data governance and AI governance models application support models runbooks and delivery assurance plans.
• Innovation roadmaps for data modernization AI / GenAI adoption application modernization AIOps and agentic AI-enabled business operations.
• Bid content for major pursuits including architecture value approach risks assumptions differentiation executive presentations and technical annexes.

Qualifications and Experiences

• Bachelors degree in engineering computer science data science information systems or equivalent; masters in business technology analytics or executive management preferred.
• 15 years of experience in enterprise architecture data architecture AI / analytics architecture applications architecture solution architecture consulting systems integration or managed services.
• 5 years in presales deal architecture bid leadership or client-facing advisory roles designing large multi-domain / multi-tower solutions.
• Proven experience in data modernization cloud analytics AI / ML GenAI application modernization COTS / SaaS solutioning integration and operations transformation.
• Experience across the sales lifecycle from account planning and business development through opportunity strategy proposal execution negotiation and transition.
• Exposure to ANZ enterprise clients regulated industries APAC multi-country clients and global delivery models is advantageous.
• Experience orchestrating technical architects and delivery teams to optimize solution value cost risk and commercial competitiveness.
• Certifications in cloud enterprise architecture data platforms AI / ML relevant SaaS / COTS platforms agile / DevSecOps or IT service management are advantageous.

Please be informed this is a hybrid role and we are open to candidates based in Sydney Melbourne and Brisbane.