Data Platform Analytics Architect

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 analytics workloads.
• Work with Vertica, SingleStore, Cloudera, and other strategic ecosystem partners to develop:
• Enable sales and presales organizations to identify, qualify, position, and architect analytics opportunities at scale.

Required Qualifications

• 8+ years of experience in technical presales, solutions architecture, enterprise architecture, database architecture, or data engineering.
• Strong understanding of enterprise analytics and modern data architectures, including:
  • Enterprise data warehouses
  • Analytical databases
  • Data lakes
  • Data Lakehouse’s
  • Distributed data platforms
  • Object storage
  • ETL/ELT pipelines
• Experience designing or supporting large-scale analytics environments.
• Strong understanding of analytical database characteristics, including:
  • Data ingestion
  • Query processing
  • Concurrency
  • Data distribution
  • Partitioning
  • Performance
  • Capacity scaling
• Experience with one or more enterprise analytics platforms such as:
  • Vertica
  • SingleStore
  • Cloudera
  • Teradata
  • Oracle/Exadata
• Demonstrated ability to lead architecture discussions from data and workload requirements rather than starting with infrastructure.
• Ability to map end-to-end data flows across ingestion, transformation, storage, analytics, and consumption.
• Experience evaluating modernization strategies for traditional database or data warehouse environments.
• Ability to understand and evaluate performance requirements for large-scale analytical workloads.
• Proven ability to lead customer discovery and translate business and technical requirements into solution architectures.
• Strong communication skills with the ability to engage both deeply technical and executive audiences.

Preferred / Nice-to-Have Experience

• Hands-on experience with Vertica, SingleStore, or Cloudera and familiarity to log analytics platforms such as Splunk and Elastic.
• Experience with Teradata or Oracle Exadata environments and modernization initiatives.
• Experience designing or migrating large enterprise data warehouses.
• Familiarity with open data lake and Lakehouse technologies and architectures.
• Familiarity with technologies such as Apache Iceberg and Spark, Trino, and modern data catalogs.
• Experience with cloud and hybrid analytics platforms.
• Experience developing TCO or business cases for database and data platform modernization.
• Experience working with Product Management, strategic ISVs, or influencing product strategy.

What Success Looks Like

• Establishing X10K as a strategic data platform for enterprise analytics rather than simply a storage target.
• Creating repeatable strategies for penetrating and displacing legacy architectures.
• Building strong joint solution motions around Vertica, SingleStore, Splunk, and Elastic.
• Helping customers successfully transition from traditional data warehouse architectures toward modern data lakehouse and open data architectures.
• Positioning solutions with precision—winning opportunities where the architecture provides meaningful technical and business advantages.
• Enabling field teams to confidently identify, qualify, size, and position analytics opportunities.
• Influencing X10K product direction based on real-world analytics customer requirements.
• Creating repeatable reference architectures, sizing methodologies, migration patterns, and competitive plays that can be scaled globally.

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 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.

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