Principal Engineer, Storage Data Science and Analytics

In the HPE Hybrid Cloud, we lead the innovation agenda and technology roadmap for all of HPE. This includes managing the design, development, and product portfolio of our next-generation cloud platform, Green Lake. Working with customers, we help them reimagine their information technology needs to deliver a simple, consumable solution that helps them drive their business results. Join us redefine what’s next for you. Technical Leadership & Strategy 
 
What you’ll do 
 As a Principal Engineer for Data Science & Analytics, you will serve as the premier technical authority bridging advanced machine learning, GenAI, and big data analytics with HPE's next-generation cloud and storage ecosystem (HPE GreenLake and hybrid cloud). 
  

In this role, you will lead the architectural strategy to extract, analyze, and operationalize intelligence from vast telemetry data pipelines generated by enterprise file, block, and object storage systems. Your work will directly impact time-to-market, cost reduction, and predictive data management frameworks (including AIOps, predictive infrastructure failure, storage deduplication, and quality of service tuning). 

What you need to bring 

• Architectural Vision: Define the organization-wide data architecture strategy and analytical roadmaps for software systems running across HPE’s hybrid cloud platform. 

 
Engineering & Model Development 

• Scale & Deploy: Design, deploy, and scale machine learning and deep learning code to run reliably in worldwide production edge-to-cloud environments. 

• Pipeline Design: Collaborate with data engineering to establish standardized ELT patterns, big data storage views, and high-throughput, low-latency streaming pipelines (supporting Kafka, Spark, etc.). 

• Generative AI & Agentic Workflows: Integrate advanced LLM workflows, Retrieval-Augmented Generation (RAG), and agentic systems into storage customer support analytics and digital products to automate troubleshooting. 

 
Collaboration & Governance 

• Cross-Functional Orchestration: Partner with product management, storage hardware engineers, and business executives to translate abstract business challenges into production-ready analytical solutions. 

• Mentorship & Culture: Provide career guidance, conduct design reviews, and actively mentor senior engineers and data scientists across multiple Scrum teams to foster an innovative technical community. 

 
Core Requirements & Qualifications 

Education & Experience 

• Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Mathematics, or a highly technical, data-oriented discipline. 

• Experience: 10+ years of proven industry experience in software product development or enterprise data science, with a heavy emphasis on distributed systems, storage, or cloud infrastructure architectures. 

 
Must Have Skills & Technical Proficiency 

• Core Data Science & ML: Advanced mastery of machine learning algorithms (time-series forecasting, clustering, anomaly detection, random forests) and deep learning frameworks. 

• Programming & Systems: Expert Python/Go-lang programmer. Strong working knowledge of data structures, algorithmic complexity, and multi-threaded/mul-process programming.  

• Generative AI: Concrete experience building and fine-tuning LLMs, prompt engineering, and leveraging vector databases. 

• Application Platform design and deployment: Experience in building scalable data pipeline design, and deployment in a multi-node environment 

Nice to have skills: 

• Container Orchestration: Master Kubernetes or equivalent systems to manage containerized workloads dynamically 

• Storage Optimization: Develop predictive, prescriptive, and generative AI models to map data paths, enhance memory/space management, and predict capacity or hardware failure across global enterprise clusters. 

• AIOps & Smart Telemetry: Validate highly complex, distributed telemetry data from customer storage networks to uncover structured insights that drive automated mitigation and system reliability. 

• Experience with Enterprise Storage domain  and C/C++ or system internals is a strong differentiator. 

 
Soft Skills & Innovation 

• Communication: Exceptional ability to explain highly technical architectural trade-offs, algorithms, and AI solutions clearly to non-technical business leaders and executive management. 

• IP Generation: A proven track record of industry innovation, backed by whitepapers, industry conference contributions, or patents in software and analytical design.