Sr Director, Software Engineer

NetApp•San Jose, CA

About The Position

DataPelago is building a data-processing platform that applies accelerated computing to analytics and AI workloads, bringing high-performance compute closer to enterprise data. Following its acquisition by NetApp, DataPelago combines an early-stage company’s focused, startup-style operating model with NetApp’s global customer reach, resources, and enterprise relationships. The organization is building a scalable business and go-to-market motion in a zero-to-one environment. 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 highly technical leadership role has direct accountability for engineering execution, product quality, and performance. The leader will contribute to architecture and critical design decisions, diagnose complex systems problems, guide senior engineers, and build a strong organization with clear ownership and predictable execution. The role works closely with principal architects, product management, and customer-facing teams. The successful candidate will combine hands-on technical leadership with organizational building, bringing contemporary expertise in AI infrastructure hardware and software, frameworks, AI agent frameworks and protocols, as well as the ability to develop a high-performing engineering team. The role suits a builder who is energized by ambiguity, moves quickly, works directly with customers, and takes ownership beyond traditional role boundaries.

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 such as PyTorch or TensorFlow.
Experience with modern data and AI ecosystems, including vector processing, search, and graph technologies.
Track record of improving engineering delivery, software quality, operational reliability, and performance in complex products or platforms.
Ability to navigate difficult architectural and development decisions while delegating effectively and empowering strong engineers and managers.
Strong communication, integrity, urgency, and ownership, with the ability to thrive in a fast-moving, evolving organization.

Nice To Haves

Familiarity with AI accelerators such as TPU, IPU, or LPU.
Experience with storage or data platforms, AI infrastructure, or large-scale ML systems.
Experience in both an entrepreneurial engineering environment and a scaled enterprise organization.

Responsibilities

Own end-to-end engineering delivery, from design through implementation and testing to production reliability.
Provide technical leadership, conduct design reviews, and guide decisions about performance, scalability, security, and system trade-offs, 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 an execution culture focused on engineering velocity, transparent progress, timely decisions, and accountability for commitments.
Recruit, coach, and retain 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 turn field feedback into actionable engineering priorities.