At HP, this role focuses on leading quality-driven data engineering across application projects, with emphasis on enterprise data architecture, scalable platforms, and governance. You will help shape how data is stored, integrated, accessed, and secured, while enabling AI/ML use cases through production-ready pipelines and reusable frameworks.
This position is based onsite in Spring, TX and supports cross-functional collaboration to turn business and transformation goals into durable platform capabilities.
What You’ll Do
- Design the enterprise-wide blueprint for how data is stored, integrated, accessed, and governed.
- Manage technical data platforms that enable downstream insights, solutions, and analytics.
- Design PS Quality data warehouses and data lakes.
- Determine architectural patterns, including medallion architecture, data mesh, and data fabric.
- Establish data standards and automated interoperability rules.
- Design data warehouses and data lakes aligned to Quality Business Requirements.
- Define and implement enterprise-grade data architectures for batch, streaming, and real-time systems for large-scale structured and unstructured data.
- Build 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 using telemetry, operational, and product data.
- Drive data monetization, standardization, and governance frameworks.
- Define a roadmap for modern data stack adoption, including cloud-native, lakehouse, streaming, and GenAI-ready architectures.
- Partner with Data Scientists to productionize ML/AI models into scalable systems.
- Build and optimize data pipelines, feature engineering frameworks, and MLOps workflows.
- Lead design, development, and deployment of complex data pipelines and distributed systems.
- Drive adoption of new technologies such as GenAI, agentic systems, streaming architectures, and data mesh.
- Ensure performance, reliability, and cost optimization goals are met.
- Ensure adherence to data governance, privacy, security, and compliance standards aligned 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 the data lifecycle management process.
- 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, representing the organization in industry forums, publications, and innovation initiatives.
- Translate business goals into platform capabilities, including faster automated analytics, enhanced AI/ML readiness, self-service tools, and operational reporting.
- Provide expertise to functional project teams and participate in cross-functional initiatives as needed.
- Work on complex problems where analysis requires an in-depth evaluation of multiple factors.
Requirements
- Education: Four-year or Graduate Degree.
- Degree area: Computer Science, Information Systems, Engineering, Statistics/Mathematics, Machine Learning, Data Analytics, and demonstrated competence.
- Experience: 7-10 years of work experience, preferably in analytics, data science, reporting, or a related field.
- Strong experience with cloud platforms: AWS and Azure (data services, analytics, storage).
- Strong experience with data platforms: Data Lakes, Lakehouse, and Data Warehousing.
- Strong experience in ETL/ELT and pipeline orchestration.
- Mandatory: Python and SQL.
- Good to have: Scala or Java.
- Experience with Streaming and real-time data systems.
- Experience with Data modeling and governance.
- Experience with MLOps and model deployment pipelines.
- Experience with modern architectures such as Data Mesh, Medallion, and API-driven data services.
Technologies
- AWS, Azure
- Python, SQL, Scala, Java
- Apache Spark
- NoSQL
- ETL, ELT
- Data Lakes, Lakehouse, Data Warehousing
- Medallion architecture, data mesh, data fabric
- MLOps
- GenAI, agentic systems, streaming architectures
Benefits
- Health insurance, Dental insurance, Vision insurance
- Long term/short term disability insurance
- Employee assistance program
- Flexible spending account
- Life insurance
- 4-12 weeks fully paid parental leave based on tenure
- 11 paid holidays
- Additional flexible paid vacation and sick leave (US benefits overview)
Preferred Certifications
- Data Analytics Certifications
Additional Knowledge & Skills
- Agile Methodology
- Automation, Big Data, Computer Science
- Data Analysis, Data Architecture, Data Engineering
- Data Modeling, Data Warehousing
- Extract Transform Load (ETL)
- Machine Learning
- NoSQL, Scalability
- Software Engineering
- Extract Transform Load (ETL)
- Java (Programming Language)
- SQL (Programming Language)
Cross-Org Skills
- Effective Communication
- Results Orientation
- Learning Agility
- Digital Fluency
- Customer Centricity
Schedule: Full time
Shift premium: No shift premium (United States of America)
Travel: 25%
Relocation: Yes
Job category: Data & Information Technology
Compensation: USD 105,050 - 161,800 per year
Experience minimum: 7 years
Location: Spring, TX (onsite)
