Principal Data Architect

We are seeking a Data Engineering Architect who will lead both enterprise data platform architecture and data strategy to enable scalable AI/ML, telemetry analytics, and business intelligence solutions.

This role goes beyond traditional data engineering, requiring end-to-end ownership of data ecosystems, from ingestion to insights, and the ability to translate business priorities into scalable, AI-ready data strategy.

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

Key Responsibilities

Data Architecture Strategy

• Design the enterprise-wide blueprint for how data is stored, integrated, accessed, and governed
• Manage the technical platforms that enable downstream insights, solutions, etc
• Design PS Quality data warehouses / data lakes
• Determine architectural patterns (e.g., medallion architecture, data mesh, data fabric)
• Establish data standards and automated interoperability rules

Data Architecture & Platform Leadership

• Designing data warehouses / data lakes that meets Quality Business Requirements
• Define and implement enterprise-grade data architectures (batch, streaming, real-time) for large-scale structured and unstructured data.
• Design scalable, secure, and high-performance data platforms supporting BI, advanced analytics, and AI/ML use cases.
• Establish data modeling standards, and reusable frameworks across the organization.

Data Strategy & Transformation

• Lead enterprise data strategy, aligning data initiatives with business, AI, and digital transformation goals.
• Identify and prioritize high-value analytics and AI opportunities leveraging telemetry, operational, and product data.
• Drive data monetization, standardization, and governance frameworks.
• Define roadmap for modern data stack adoption (cloud-native, lakehouse, streaming, GenAI-ready architectures).

AI/ML Enablement & Industrialization

• Partner closely with Data Scientists to productionize ML/AI models into scalable systems.
• Build and optimize data pipelines, feature engineering frameworks, and MLOps workflows.

Engineering Execution & Innovation

• Lead the design, development, and deployment of complex data pipelines and distributed systems.
• Drive adoption of new technologies (GenAI, agentic systems, streaming architectures, data mesh).
• Ensure solutions meet performance, reliability, and cost optimization goals.

Governance, Security & Compliance

• Ensure adherence to data governance, privacy, security, and compliance standards in alignment with HP Cybersecurity and privacy guidlines
• Maintain master data management, access controls, audits, metadata, management, and data hierarchy
• Establish data quality frameworks, lineage, observability, and monitoring mechanisms.
• Implement best practices across data lifecycle management.

Cross-Functional Leadership & Influence

• Influence executive leadership, architecture boards, and cross-functional stakeholders on data strategy decisions.
• Act as a thought leader in data engineering and AI data ecosystems.
• Represent the organization in industry forums, publications, and innovation initiatives.

Business Alignment

• Translate business goals into platform capabilities
• Faster automated analytics
• Enhanced AI/ML readiness
• Self-Service Tools
• Operational Reporting
• Enable data-driven decision making
  

Technical Expertise

• Strong experience in:
• Cloud platforms: AWS, Azure (data services, analytics, storage)
• Data platforms: Data Lakes, Lakehouse, Data Warehousing
• ETL/ELT and pipeline orchestration
• Programming:
• Python, SQL (mandatory)
• Scala/Java (good to have)
• Experience with:
• Streaming and real-time data systems
• Data modeling and governance
• MLOps / model deployment pipelines
• Modern architecture (Data Mesh, Medallion, API-driven data services)
  

Knowledge & Skills

• Agile Methodology
• Amazon Web Services
• Apache Hadoop
• Apache Kafka
• Apache Spark
• Big Data
• Computer Science
• Data Analysis
• Data Engineering
• Data Modeling
• Data Pipelines
• Data Warehousing
• Extract Transform Load (ETL)
• Java (Programming Language)
• Machine Learning
• Microsoft Azure
• Python (Programming Language)
• Scala (Programming Language)
• Scalability
• SQL (Programming Language)

Cross-Org Skills

• Effective Communication
• Results Orientation
• Learning Agility
• Digital Fluency
• Customer Centricity

Impact & Scope

• Impacts function and leads and/or provides expertise to functional project teams and may participate in cross-functional initiatives.

Complexity

• Works on complex problems where analysis of situations or data requires an in-depth evaluation of multiple factors.

Disclaimer

• This job description describes the general nature and level of work performed in this role. It is not intended to be an exhaustive list of all duties, skills, responsibilities, knowledge, etc. These may be subject to change and additional functions may be assigned as needed by management.

The pay range for this role is $147,050 to $230,850 USD annually with additional opportunities for pay in the form of bonus and/or equity (applies to United States of America candidates only). Pay varies by work location, job-related knowledge, skills, and experience.

Benefits

HP offers a comprehensive benefits package for this position, including:

• Health insurance
• Dental insurance
• Vision insurance
• Long term/short term disability insurance
• Employee assistance program
• Flexible spending account
• Life insurance
• Generous time off policies, including;
• 4-12 weeks fully paid parental leave based on tenure
• 11 paid holidays
• Additional flexible paid vacation and sick leave (US benefits overview)

The compensation and benefits information is accurate as of the date of this posting. The Company reserves the right to modify this information at any time, with or without notice, subject to applicable law.