# Data Platform Analytics Architect at Hewlett Packard Enterprise

Hewlett Packard Enterprise is hiring a Data Platform Analytics Architect for a remote role open to applicants in United States. Level rates it Little AI ●○○○; you can [apply on Hewlett Packard Enterprise](https://jobsbylevel.com/go/b0266d21-eaaf-4060-ae36-e551291f3c5d?utm_medium=ai-agent).

AI Level 1, AI centrality 0 out of 100. Remote (All, Michigan, United States of America).

## Details

- Company: [Hewlett Packard Enterprise](https://jobsbylevel.com/companies/hewlett-packard-enterprise)
- AI level: AI Level 1 (score 0 out of 100)
- Location: Remote (All, Michigan, United States of America)
- Posted: September 1, 2026
- Apply: https://jobsbylevel.com/go/b0266d21-eaaf-4060-ae36-e551291f3c5d?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=data-platform-analytics-architect-at-hewlett-packard-enterprise-6eae9f

## Prepare for this job

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## Description

Data Platform Analytics Architect This role has been designated as ‘Remote/Teleworker’, which means you will primarily work from home. 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: We are seeking a highly strategic and technically grounded Data Platform Analytics Solutions Architect to help drive the next phase of growth for our X10K data platform business. This is a customer-facing architect role that leads with a data and workload-first perspective, focusing on how enterprise data is ingested, stored, processed, queried, governed, and consumed across modern analytics environments. The role will operate at the intersection of enterprise analytics, data architecture, modern data platforms, and infrastructure, helping customers modernize traditional data warehouse environments into more open, scalable, performant, and flexible data lake and data Lakehouse architectures. A major focus will be identifying and developing opportunities around analytics platforms such as Vertica, SingleStore, and Cloudera, while helping customers evaluate alternatives to traditional tightly coupled legacy Enterprise Data Warehouse platforms. The Architect will help customers understand where modern architectures that separate compute from data can improve scalability, economics, operational flexibility, and long-term data accessibility. You will partner closely with customers, sales teams, solution architects, ISVs, and Product Management to design high-impact analytics solutions, develop migration and modernization strategies, and build repeatable go-to-market motions around X10K. A key aspect of the role is the ability to identify and prioritize the right opportunities based on workload characteristics, customer readiness, technical fit, scale, economics, and business objectives. The successful candidate must be able to move beyond infrastructure discussions and engage customers at the data architecture, database, analytics, and application level. This is not a pure storage role. A strong understanding of storage and data infrastructure is important, but the architect must be able to position X10K as part of a broader enterprise data architecture and demonstrate how the data platform enables analytics modernization. Key Responsibilities Data-Centric Analytics Architecture Lead architecture discussions starting with the customer's data, applications, and analytics requirements, including: Data volumes, growth rates, velocity, and distribution Structured, semi-structured, and unstructured data Data ingestion and transformation requirements Query patterns and analytical processing requirements Data locality, gravity, movement, and duplication Data retention and lifecycle requirements Batch, streaming, and real-time analytics requirements Concurrency and performance expectations Design modern analytics architectures by optimizing: Data ingestion and ETL/ELT pipelines Compute-to-data access patterns Query performance and concurrency Data placement and movement Storage and compute scalability Open data access and interoperability Data lifecycle and retention Performance, reliability, and cost efficiency Help customers evolve from infrastructure-centric architectures toward data-centric architectures where multiple analytics engines and applications can leverage common enterprise data assets.

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