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Principal Data Architect

Job in Spring, Harris County, Texas, 77391, USA
Listing for: Information Technology Senior Management Forum
Full Time position
Listed on 2026-06-26
Job specializations:
  • IT/Tech
    Data Engineering, Data Science Manager, Data Warehousing, Data Analyst
Job Description & How to Apply Below

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 meet 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 product ionize 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 guidelines.
  • 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…
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