Principal Data Architect

HPSpring, TX
$147,050 - $230,850Remote

About The Position

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.

Requirements

  • 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)
  • Streaming and real-time data systems
  • Data modeling and governance
  • MLOps / model deployment pipelines
  • Modern architecture (Data Mesh, Medallion, API-driven data services)
  • 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)
  • Effective Communication
  • Results Orientation
  • Learning Agility
  • Digital Fluency
  • Customer Centricity

Responsibilities

  • 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
  • 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.
  • 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).
  • Partner closely with Data Scientists to productionize ML/AI models into scalable systems.
  • Build and optimize data pipelines, feature engineering frameworks, and MLOps workflows.
  • 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.
  • 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.
  • 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.
  • Translate business goals into platform capabilities
  • Faster automated analytics
  • Enhanced AI/ML readiness
  • Self-Service Tools
  • Operational Reporting
  • Enable data-driven decision making

Benefits

  • 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
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