Sr Director - Software Engineer

Job Summary

About DataPelago and NetApp

DataPelago is building a new data-processing platform applying accelerated computing for the AI era. Its technology is designed to accelerate analytics and AI workloads by bringing high-performance compute closer to enterprise data.

Following its acquisition by NetApp, DataPelago combines the ownership, pace, and influence of an early-stage company with NetApp's global customer reach, resources, and enterprise relationships. DataPelago will continue operating with a focused, startup-style model while it builds a scalable business and go-to-market motion.

This is a zero-to-one environment. We are looking for builders who are energized by ambiguity, move quickly, work directly with customers, and take ownership beyond traditional role boundaries. The strongest candidates will ideally bring experience from both early-stage companies and established organizations. They will know how to build with speed while also creating the processes, partnerships, and operating discipline needed to scale successfully within a larger enterprise.

The Senior Director of Engineering will build and lead the engineering organization responsible for defining, building, and delivering complex data and AI infrastructure software. This is a highly technical leadership role with direct accountability for engineering execution, product quality, and performance.

The leader will bring the technical depth to engage in architecture and critical design decisions, diagnose complex systems problems, and guide senior engineers, while also building a strong team and establishing clear ownership and predictable execution. They will work closely with principal architects, product management, and customer-facing teams.

The ideal candidate is a hands-on technical leader at heart and an effective organizational builder: equally comfortable debating technical trade-offs, with contemporary expertise in AI infrastructure hardware, software frameworks, AI Agent frameworks, protocols, and developing a high-performing engineering team.

Job Requirements

• Proven record of designing and delivering sophisticated production software, with a strong foundation as a hands-on engineer, systems architect, or technical leader.
• Deep technical knowledge of hardware-software co-design, distributed systems, performance engineering, and large-scale data processing.
• Expertise in GPU computing and acceleration, including GPU architecture, parallel execution, memory and bandwidth trade-offs, profiling, and CUDA or comparable GPU software stacks.
• Experience with AI model processing frameworks like PyTorch, TensorFlow.
• Experience with modern data and AI ecosystems, including vector processing and search and graph technologies.
• Familiarity with other AI accelerators like TPU, IPU, LPU, etc.
• Track record of improving engineering delivery, software quality, operational reliability, and performance in complex products or platforms.
• Ability to navigate tough architectural decisions and development choices, while delegating effectively and empowering strong engineers and managers.
• Strong communication, integrity, urgency, and ownership; ability to thrive in a fast-moving, evolving organization.
• Experience with storage/data platforms, AI infrastructure, or large-scale ML systems is a plus.
• Experience in both an entrepreneurial engineering environment and a scaled enterprise organization is preferred.

What You'll Do

• Own end-to-end engineering delivery, from design through implementation, testing, and production reliability.
• Provide deep technical leadership, design reviews, and guidance on performance, scalability, security, and system tradeoffs, including accelerated computing where relevant; partner with senior architects to turn technical direction into robust products.
• Translate product priorities and customer commitments into realistic engineering plans with clear owners, milestones, dependencies, quality gates, and measurable outcomes.
• Build a disciplined execution culture with high engineering velocity, transparent progress, timely decisions, and accountability for commitments.
• Recruit, coach, and retain exceptional engineers and engineering leaders; define team structures, clarify decision rights, develop successors, and raise the technical bar.
• Balance near-term enterprise customer needs with maintainable architecture, technical debt reduction, platform investments, and sustainable innovation.
• Partner with product management, architecture, Solution Architects, and customer-facing teams to validate requirements and convert field feedback into actionable engineering priorities.