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Eda Manager Jobs in Raleigh, NC (NOW HIRING)

... afirst-party, fully managed enterprise file service on Microsoft Azure, delivered in deep ... Analytics and simulation adjacencies (HPC/EDA-style throughput, shared filesystem semantics ...

... first-party, fully managed enterprise file service on Microsoft Azure, delivered in deep ... Analytics and simulation adjacencies (HPC/EDA-style throughput, shared filesystem semantics ...

Senior HPC and LSF Operations Engineer

Durham, NC · Hybrid

$101K - $138K/yr

Manage, scale, and optimize job scheduling systems (LSF, Slurm, etc.) in a large-scale, multi-site environment supporting EDA and other compute-intensive workloads * Analyze scheduler and ...

Follow the change management procedures and policies. Qualifications * 5+ years of ... Oracle, Informix and EDA * Knowledge of various integration concepts including: Business-to ...

Follow the change management procedures and policies. Qualifications * 5+ years of ... Oracle, Informix and EDA * Knowledge of various integration concepts including: Business-to ...

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Eda Manager information

What is the difference between Eda Manager vs Eda Engineer?

AspectEda ManagerEda Engineer
CredentialsBachelor's or Master's in Electrical Engineering, Project Management certificationsBachelor's or Master's in Electrical Engineering, relevant technical certifications
Work EnvironmentOversees teams, manages projects, coordinates with clientsDesigns and tests electrical systems, performs simulations
Industry UsageUsed in project planning, team leadership, and client communicationUsed in circuit design, simulation, and technical analysis

The main difference between an Eda Manager and an Eda Engineer lies in their roles. The Eda Manager oversees projects and teams, focusing on management and coordination, while the Eda Engineer handles technical design and analysis. Both roles require electrical engineering credentials, but their responsibilities and daily tasks differ significantly.

What are the key skills and qualifications needed to thrive as an EDA Manager, and why are they important?

To thrive as an EDA Manager, you need strong expertise in electronic design automation (EDA) methodologies, semiconductor design processes, and a relevant engineering degree. Familiarity with EDA tools such as Cadence, Synopsys, and Mentor Graphics, as well as project management certifications, is often required. Excellent leadership, problem-solving, and communication skills help manage teams and coordinate complex projects. These qualifications ensure efficient workflow, high-quality chip design, and successful project delivery in the fast-paced semiconductor industry.

What are the main challenges an EDA Manager faces when overseeing cross-functional design teams?

An EDA Manager often encounters challenges in aligning the goals and timelines of diverse engineering teams, such as digital, analog, and verification groups, who rely on different EDA tools and methodologies. Ensuring smooth integration of workflows, maintaining up-to-date tool expertise, and resolving toolchain compatibility issues are key aspects of the role. Additionally, EDA Managers must balance project deadlines with the need for continuous process improvement, often acting as a bridge between technical teams and upper management to communicate progress and resource needs effectively.

What are EDA Managers?

EDA Managers are professionals who oversee Electronic Design Automation (EDA) projects within semiconductor or electronics companies. Their primary responsibilities include managing teams that use specialized software tools to design, verify, and test integrated circuits (ICs) and other electronic systems. EDA Managers coordinate between engineering, software, and hardware teams to ensure project deadlines are met and quality standards are maintained. They also keep up-to-date with the latest EDA tools and methodologies to enhance productivity and optimize design flows.
What are the most commonly searched types of Eda jobs in Raleigh, NC? The most popular types of Eda jobs in Raleigh, NC are:
What are popular job titles related to Eda Manager jobs in Raleigh, NC? For Eda Manager jobs in Raleigh, NC, the most frequently searched job titles are:
What cities near Raleigh, NC are hiring for Eda Manager jobs? Cities near Raleigh, NC with the most Eda Manager job openings:
Principal Product Manager

Principal Product Manager

NetApp

Morrisville, NC • On-site

Other

Medical, Life, Retirement, PTO

Re-posted 16 days ago


Job description

Job Summary

NetApp is hiring aprincipal-level product leaderto own theAI product strategyforAzure NetApp Files (ANF)-afirst-party, fully managed enterprise file service on Microsoft Azure, delivered in deep partnership between NetApp and Microsoft. In the spirit of NetApp's"business builder"cloud roles, you will translate a fast-moving AI landscape intodifferentiated platform capabilities,joint roadmap betswith Microsoft, andenterprise outcomes(performance, data locality, governance, and time-to-value for AI pipelines). 

You will sit at the intersection ofenterprise storage,Azure AI infrastructure, andindustry AI workloads, ensuring ANF is positioned and built as astrategic data foundationfor training, inference, RAG, analytics, simulation, and agentic workflows-without forcing customers to abandon enterprise file semantics, protection, or hybrid operating models. 

Role Overview

We need ahighly strategic and deeply technicalprincipal PM who can: 

  • Definemulti-year AI vision and roadmapfor ANF in the context of Azure AI services, GPU estates, data platforms, and regulated enterprise environments. 

  • Turn emerging patterns (LLMs, RAG, agents, orchestration, multimodal data, vector retrieval, high-throughput checkpointing) intoconcrete product requirementsandjoint go-to-marketnarratives with Microsoft. 

  • Balancehyperscaler co-developmentconstraints withNetApp differentiation(enterprise data services, multiprotocol access, lifecycle management, resiliency, and cross-cloud consistency where relevant). 

Responsibilities

AI strategy & roadmap 

  • Own end-to-endAI strategyfor ANF: problem selection, success metrics, phased delivery, and competitive positioning vs. other Azure and AI-native storage options. 
  • Prioritize investments acrossperformance,scale,data services,protocol and API surfaces, andoperational excellencefor AI pipelines. 

Workload-led product definition 

Drive requirements for AI-centric scenarios, including: 

  • Training and inference data planes (high throughput, low latency, checkpointing, bursty I/O) 
  • RAG and enterprise search (datasets, versioning, clones, refresh patterns) 
  • Agentic workflows and orchestration (durable shared state, tool/data access patterns-where productized responsibly) 
  • Large multimodal and enterprise datasets (governance, access control, lifecycle) 
  • Analytics and simulation adjacencies (HPC/EDA-style throughput, shared filesystem semantics) 

Hyperscaler & ecosystem partnership 

  • Partner withMicrosoftteams acrossAzure AI / Foundry,Azure Machine Learning,AKS / container platforms,GPU infrastructure,data/analytics(e.g. Databricks-style patterns on Azure), and core Azure storage/networking dependencies. 
  • Align ANF's AI story withAzure-wide AI dataguidance and reference architectures, and feedreal customer workload evidenceback into joint planning. 

Cross-functional leadership 

  • Lead acrossengineering, product marketing, sales, customer success, and professional servicesto ship capabilities and repeatablereference architectures / proof points. 
  • Engagestrategic customersanddesign partnersto validate pain, quantify value, and de-risk roadmap bets. 

Market intelligence & evangelism 

  • Monitor AI infrastructure trends (models, frameworks, orchestration, data formats) and competitor moves; translate intodifferentiated bets. 
  • Represent ANF as acredible technical executivein briefings, advisory councils, and industry forums. 

Industry segmentation 

  • Tailor AI storage strategy for segments where file semantics and performance matter, for example:semiconductor/EDA,manufacturing,healthcare imaging,financial services,energy,media & entertainment, andHPC/simulation-including compliance and data residency realities. 

AI strategy & roadmap 

  • Own end-to-endAI strategyfor ANF: problem selection, success metrics, phased delivery, and competitive positioning vs. other Azure and AI-native storage options. 
  • Prioritize investments acrossperformance,scale,data services,protocol and API surfaces, andoperational excellencefor AI pipelines. 

Workload-led product definition 

Drive requirements for AI-centric scenarios, including: 

  • Training and inference data planes (high throughput, low latency, checkpointing, bursty I/O) 
  • RAG and enterprise search (datasets, versioning, clones, refresh patterns) 
  • Agentic workflows and orchestration (durable shared state, tool/data access patterns-where productized responsibly) 
  • Large multimodal and enterprise datasets (governance, access control, lifecycle) 
  • Analytics and simulation adjacencies (HPC/EDA-style throughput, shared filesystem semantics) 

Hyperscaler & ecosystem partnership 

  • Partner withMicrosoftteams acrossAzure AI / Foundry,Azure Machine Learning,AKS / container platforms,GPU infrastructure,data/analytics(e.g. Databricks-style patterns on Azure), and core Azure storage/networking dependencies. 
  • Align ANF's AI story withAzure-wide AI dataguidance and reference architectures, and feedreal customer workload evidenceback into joint planning. 

Cross-functional leadership 

  • Lead acrossengineering, product marketing, sales, customer success, and professional servicesto ship capabilities and repeatablereference architectures / proof points. 
  • Engagestrategic customersanddesign partnersto validate pain, quantify value, and de-risk roadmap bets. 

Market intelligence & evangelism 

  • Monitor AI infrastructure trends (models, frameworks, orchestration, data formats) and competitor moves; translate intodifferentiated bets. 
  • Represent ANF as acredible technical executivein briefings, advisory councils, and industry forums. 

Industry segmentation 

  • Tailor AI storage strategy for segments where file semantics and performance matter, for example:semiconductor/EDA,manufacturing,healthcare imaging,financial services,energy,media & entertainment, andHPC/simulation-including compliance and data residency realities. 
Job Requirements

Required 

  • 10+ yearsproduct management incloud infrastructure,enterprise storage,AI/ML infrastructure, ordata platforms(principal scope: portfolio strategy, multi-team alignment, executive storytelling). 
  • Strong command ofenterprise storage: NFS/SMB semantics, snapshots/clones, replication, backup integration patterns, capacity/performance tiers, and large-scale filesystem behavior under parallel workloads. 
  • Hands-on familiarity withmodern AI stacks: LLMs, RAG architectures, embeddings/vector retrieval patterns, training vs. inference IO profiles, orchestration, and enterprise AI data pipelines. 
  • Demonstrated success influencingengineering and partner roadmapswithout direct authority; experience withhyperscaler first-partyor deeply partnered services is a strong plus. 
  • Excellent written and verbal communication tocustomers, executives, and engineers. 

Preferred 

  • Direct experience withMicrosoft AzureAI services,GPUestates on Azure, and/orAzure Kubernetes Service+ ML platform integrations. 
  • Familiarity withDatabricks,Iceberg/Delta-class open table patterns,Kubernetesstorage patterns,NVIDIA AIsoftware stacks, andenterprise MLOpsrelease cadences. 
  • Background inregulated industriesand enterprise security/governance requirements for AI data. 
Education
  • MBAor advanced degree inCS/Engineering(helpful, not a substitute for demonstrated technical depth). 

Compensation:
The target salary range for this position is $228,000 - $325,000. The salary offered will be determined by the candidate's location, qualifications, experience, and education and may be outside of this range. Final compensation packages are competitive and in line with industry standards, reflecting a variety of factors, and include a comprehensive benefits package. This may cover Health Insurance, Life Insurance, Retirement or Pension Plans, Paid Time Off, various Leave options, Performance-Based Incentives, employee stock purchase plan, and/or restricted stocks (RSU's), with all offerings subject to regional variations and governed by local laws, regulations, and company policies. Benefits may vary by country and region, and further details will be provided as part of the recruitment process.