Principal Engineer, Storage Data Science and Analytics
Nimble Storage·Accel (Getro)
Bengaluru, IndiaPosted Jun 30, 2026
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DescriptionPrincipal Engineer, Storage Data Science and Analytics 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 & StrategyWhat 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 DevelopmentScale & 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 & GovernanceCross-Functional Orchestration: Partner with product management, storage hardware engineers, and business executives to translate abstract business challenges into production-ready analytical...