Manager, Analytics Software Engineering

NetApp, Inc.•Durham, NC

6d•$170,000 - $220,000

About The Position

NetApp is seeking a hands-on, innovative Software Engineering Manager to lead globally distributed software engineering and data science teams responsible for AI-driven analytics, recommendation systems, intelligent insights, and customer success platforms. This leader will manage teams across multiple locations, building scalable platforms that turn data into actionable intelligence. The role combines strong engineering management with practical expertise in AI, machine learning, and emerging technologies. The ideal candidate is a hands-on engineering leader with the technical credibility to guide architecture, solve complex problems, and unblock teams, as well as the organizational leadership to develop people, align distributed teams, and deliver measurable outcomes across a diverse portfolio. We are looking for someone who is: An AI-first leader who combines technical credibility with the ability to build, coach, and scale distributed engineering and data science teams. A practical executor who balances innovation, speed, quality, security, scalability, and operational discipline. A business-focused partner who connects technology investments to measurable customer, product, and organizational impact. This player-coach combines people leadership with technical depth, an AI-first mindset, and a bias for action. They will guide architecture and critical decisions, stepping in selectively to prototype, troubleshoot, or unblock teams while maintaining accountability through technical leads.

Requirements

Bachelor’s or master’s degree in computer science, engineering, data science, or a related field, or equivalent practical experience.
8+ years in software engineering, data platforms, data science, or related disciplines, including 4+ years managing cross-functional technical teams and leading senior engineers, technical leads, or managers.
Experience leading globally distributed teams with clear ownership, effective decision-making, and reliable delivery across time zones.
Proven delivery of enterprise-scale, data-intensive, or AI-enabled products from concept through production.
Strong understanding of software architecture, APIs, data pipelines, cloud platforms, and production operations, with the ability to assess trade-offs across software, data, and AI systems.
Working knowledge of machine learning, predictive modeling, Generative AI, and the lifecycle for deploying, monitoring, and governing AI-enabled solutions.
Ability to translate ambiguous business problems into clear technical approaches and communicate effectively with senior stakeholders.

Nice To Haves

Experience leading enterprise applications, data platforms, and analytics solutions using modern software, data, and cloud technologies; familiarity with Python, C#, .NET, SQL Server, Azure Data Factory, Azure Databricks, Spark, or Power BI is beneficial.
Experience with modern data architectures, APIs, microservices, cloud-native applications, and large-scale structured and unstructured data.
Experience building or scaling machine learning, recommendation, predictive analytics, or intelligent decision-support solutions.
Familiarity with Azure OpenAI, LLMs, RAG, AI agents, vector databases, MLOps, model monitoring, and responsible AI.
Knowledge of DevOps, CI/CD, automated testing, observability, and production operations.
Ability to evaluate emerging technologies and apply them pragmatically to business opportunities.
Remains technically engaged in architecture reviews, prototypes, design discussions, production troubleshooting, and AI strategy without becoming the teams’ primary implementer.

Responsibilities

Serve as a hands-on technical leader who earns credibility through sound judgment, ownership, and execution.
Partner with engineers, data scientists, architects, and product leaders to guide architecture, resolve critical issues, and turn ambiguity into action.
Champion responsible AI-first thinking across products, engineering productivity, and business problem-solving.
Help teams apply machine learning, Generative AI, agents, recommendation systems, and predictive models to deliver measurable value.
Promote responsible, secure, scalable AI adoption while encouraging rapid experimentation and continuous learning.
Lead and develop software engineering, data science, and technical leadership talent across multiple geographic locations.
Establish clear decision rights, technical ownership, delivery accountability, and cross-geo collaboration.
Build an inclusive, high-accountability culture and support hiring, career development, performance management, and succession planning.
Own execution and operational health across strategic products, shared platforms, and technical workstreams, balancing near-term delivery with architecture investments.
Translate priorities into outcome-based roadmaps with clear milestones, ownership, dependencies, risks, and success measures.
Ensure visible execution while balancing speed, security, quality, scalability, reliability, and production readiness.
Partner with product, architecture, data, and business leaders to shape strategy and connect engineering investments to measurable outcomes.
Promote reusable platforms, scalable patterns, pragmatic technology choices, and shared capabilities.
Build strong partnerships with product, program, architecture, analytics, and business leaders.
Communicate roadmaps, trade-offs, progress, operational health, risks, and investment needs using measurable outcomes.
Align distributed teams around shared priorities, architecture, and execution discipline.