Data Platform Analytics Architect

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.
Recommend design optimizations that improve performance, scalability, operational simplicity, resiliency, and economics.
Data Warehouse Modernization
Lead technical assessments of traditional enterprise data warehouse environments, particularly:
Identify workloads and data sets that are strong candidates for modernization.
Analyze existing environments based on capacity, performance, ETL/ELT, concurrency, licensing and make informed recommendations and solution alternatives.
Develop modernization strategies that transition customers from traditional data warehouses toward modern analytical databases and data Lakehouse architectures.
Help customers determine which workloads should be migrated, modernized, consolidated, or retained.
Partner with sales teams to create compelling competitive strategies for displacement opportunities.
Analytics Platform Architecture
Develop deep expertise in analytics and data platforms strategically aligned to X10K, including:
Enterprise analytics and Lakehouse platforms such as Vertica, SingleStore, and Cloudera
Search, observability, and machine-data analytics platforms such as Splunk and Elastic
Articulate the architectural differences between traditional shared-nothing data warehouses, distributed databases, cloud-native databases, data lakes, and Lakehouse architectures.
Help customers select the appropriate architecture based on workload requirements rather than simply replacing one infrastructure platform with another.
Work with strategic software partners to develop validated solutions, best practices, reference architectures, sizing guidance, and repeatable deployment patterns.
Analytics Solution Architecture & Sizing
Scope and size enterprise analytics environments based on data volumes, size, ingest rate, retention, SLA, and other customer specific functional requirements.
Understand how analytical database behavior impacts infrastructure requirements.
Abilit to assess and establish realistic performance expectations across the complete analytics pipeline from data ingestion through transformation, query processing, and data protection.
Develop sizing methodologies that field architects and sales teams can use consistently across analytics opportunities.
Help customers design and adopt modern data lake and data Lakehouse architectures.
Articulate the architectural and business benefits of separating enterprise data from individual compute and database platforms.
Understand and articulate concepts including:
S3/object-based data architectures
Open data formats
Open table formats
Schema evolution
Metadata and cataloging
Data governance
Data sharing
Data pipelines
Distributed analytics
Compute/storage separation
Position the data lake or Lakehouse as an enterprise data foundation, rather than simply another storage tier.
Help customers understand when separating compute from storage can provide architectural or economic advantages compared with tightly integrated database appliances.
Customer Engagement & Deal Leadership
Lead technical discovery sessions with enterprise customers to identify, shape, and qualify analytics modernization opportunities.
Translate business objectives into scalable analytics and data platform architectures.
Understand both the technology and business drivers behind major data warehouse modernization initiatives.
Serve as a trusted advisor during complex architecture and platform transformation discussions.
Apply strong technical and commercial judgment to determine where X10K and associated analytics platforms are the right fit.
Maintain credibility by clearly identifying where a proposed architecture does—and does not—align with customer requirements.
Migration & Modernization Strategy
Help customers assess and build practical migration strategies for large-scale analytics environments.
Develop phased approaches that allow customers to modernize without requiring unnecessary "big bang" migrations.
Identify opportunities where new workloads, data growth, archive data, or adjacent analytics initiatives can provide an initial entry point into an incumbent/ legacy environment.
Help customers develop migration approaches that reduce technical and operational risk while creating a path toward a broader modern data architecture.
Cross-Functional Leadership
Partner closely with Product Management to influence X10K analytics roadmap priorities, prioritize feature/functionality, and identify competitive gaps.
Partner with engineering, performance teams, ISVs, and solution teams to validate important a