AI-Driven Data Platform Architect

HP; Spring (TX); On-site; USD 147,000 - 231,000; Full time

Benefits offered by this job

Health insurance

Dental insurance

Vision insurance

Disability insurance

Employee assistance program

Flexible spending account

Life insurance

Generous paid time off

Parental leave

11 paid holidays

Paid vacation and sick leave

Job summary

HP is seeking a Principal Data Architect to lead enterprise data platform architecture and data strategy. You will own end-to-end data ecosystems from ingestion to insights, enabling scalable AI/ML, telemetry analytics, and BI solutions.

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

Qualifications

• Experience designing enterprise data architectures and platforms.
• Proven ability to translate business goals into scalable data strategy.
• Strong skills in cloud data services (AWS/Azure) and data governance.
• Proficiency in SQL and programming (Python, Java/Scala).

Responsibilities

• Design enterprise-wide data architecture blueprint for storage, integration, access and governance.
• Define multi-pattern architectures (medallion, data mesh, data fabric) and standards.
• Lead data platform development for BI, analytics, and AI/ML use cases.
• Partner with Data Science teams to productionize ML models and pipelines.
• Govern data quality, security, privacy, and compliance across the organization.

Skills

Data Architecture

Cloud platforms

SQL

Python

Java/Scala

Data Modeling

Data Pipelines

MLOps

Streaming

Data Governance

Tools

Hadoop

Kafka

Spark

Data Lakes

Lakehouse

Data Warehousing

ETL/ELT

APIs

Job description

HP is seeking a Principal Data Architect to lead enterprise data platform architecture and data strategy. You will own end-to-end data ecosystems from ingestion to insights, enabling scalable AI/ML, telemetry analytics, and BI solutions.

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