Principal Engineer, Storage Data Science and Analytics

Job description

This role has been designed as ‘’Onsite’ with an expectation that you will primarily work from an HPE office. **Who We Are** Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work. We help companies connect, protect, analyze, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today’s complex world. Our culture thrives on finding new and better ways to accelerate what’s next. We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good. If you are looking to stretch and grow your career our culture will embrace you. Open up opportunities with HPE. **Job Description** 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. - 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. 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. Hard 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 programming. Experience with C/C++ or system internals is a strong differentiator. - Generative AI: Concrete experience building and fine-tuning LLMs, prompt engineering, and leveraging vector databases. 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. **Additional Skills** Cloud Architectures, Cross Domain Knowledge, Design Thinking, Development Fundamentals, DevOps, Distributed Computing, Microservices Fluency, Full Stack Development, Release Management, Security-First Mindset, User Experience (UX) **What We Can Offer You** **Health & Wellbeing** We strive to provide our team members and their loved ones with a comprehensive suite of benefits that supports their physical, financial and emotional wellbeing. **Personal & Professional Development** We also invest in your career because the better you are, the better we all are. We have specific programs catered to helping you reach any career goals you have — whether you want to become a knowledge expert in your field or apply your skills to another division. **Unconditional Inclusion** We are unconditionally inclusive in the way we work and celebrate individual uniqueness. We know varied bac