Lead AI-Ready Data Platform Architect

Hewlett Packard Enterprise; Spring (TX); On-site; USD 147,000 - 231,000; Full time

Benefits offered by this job

Health insurance

Dental insurance

Vision insurance

Long term/short term disability

Employee assistance program

Flexible spending account

Life insurance

Generous time off (4–12 weeks paid par

11 paid holidays

Flexible paid vacation and sick leave

Job summary

Hewlett Packard Enterprise seeks a Principal Data Architect to lead enterprise data platform architecture and data strategy, enabling scalable AI/ML, telemetry analytics, and BI solutions. You will own end-to-end data ecosystems—from ingestion to insights—and translate business priorities into scalable, AI-ready data assets.

You will partner with Data Science and AI teams to design future-ready platforms, industrialize ML pipelines, and drive data as a strategic asset across the organization.

Qualifications

• Several years of experience in data architecture and engineering leadership.
• Experience designing enterprise data platforms (warehouses, lakes, lakehouse).
• Proven ability to translate business needs into scalable data strategy and AI-ready data assets.

Responsibilities

• Design enterprise-wide data architecture for large-scale data ingestion, storage, and access.
• Lead data platform initiatives including data warehouses, data lakes, and data mesh concepts.
• Partner with Data Science/AI teams to productionize ML pipelines and enable AI-ready data platforms.
• Define data governance, security, and quality standards across the organization.
• Influence leadership and stakeholders through architecture governance bodies.

Skills

Python

SQL

AWS

Azure

Data Lakes

Data Warehousing

ETL/ELT

Kafka

Spark

Hadoop

MLOps

Data Mesh

Medallion Architecture

APIs

Tools

Python

SQL

Job description

Hewlett Packard Enterprise seeks a Principal Data Architect to lead enterprise data platform architecture and data strategy, enabling scalable AI/ML, telemetry analytics, and BI solutions. You will own end-to-end data ecosystems—from ingestion to insights—and translate business priorities into scalable, AI-ready data assets.

You will partner with Data Science and AI teams to design future-ready platforms, industrialize ML pipelines, and drive data as a strategic asset across the organization.