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Senior Software Designer

Hewlett Packard Enterprise
United Statesfull_timeVerifiedPosted 26 Jan 2026
💰 $276,500/yr($136,500/yr$276,500/yr)

About the role

Senior Software Designer

  

This role has been designed as ‘’Onsite’ with an expectation that you will primarily work from an HPE office.

Who We Are:

Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work. We help companies connect, protect, analyze, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today’s complex world. Our culture thrives on finding new and better ways to accelerate what’s next. We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good. If you are looking to stretch and grow your career our culture will embrace you. Open up opportunities with HPE.

Job Description:

   

Senior Data Engineer
Location: Sunnyvale, CA (Hybrid)
CATALYST (Engineering Technologies) – Decision Support Analytics (DSA)

About the Team

The CATALYST (Engineering Technologies) organization plays a vital role in enabling HPE’s product development, validation, and release processes. Our team develops the data pipelines, analytical frameworks, and visualization platforms that provide the intelligence driving HPE’s engineering and business decisions. As part of the Decision Support &​ Analytics (DSA) team, you’ll collaborate with stakeholders across engineering, operations, and analytics to build scalable data platforms, develop ML-ready datasets, and deliver dashboards that optimize product development and business performance across HPE’s engineering ecosystem.

The Role

We’re seeking an experienced Staff Data Engineer to design, build, and maintain large-scale, high-performance data solutions that support advanced analytics and machine learning. You’ll manage full data lifecycle processes—from ingestion and transformation to modeling and visualization—ensuring that data systems are efficient, reliable, and high quality. In this role, you will guide technical direction, implement automation, and drive improvements in data accessibility and usability across HPE’s technical organizations.

Key Responsibilities

  • Architect, develop, and optimize scalable data pipelines, ETL processes, and data models for engineering telemetry and analytics use cases.

  • Collaborate with engineering teams to define data requirements and develop end-to-end analytical solutions.

  • Design and manage cloud-based data warehouse systems integrating multiple structured and unstructured sources.

  • Write and optimize code in SQL, Python, and Java to support data pipelines and applications.

  • Develop high-quality dashboards and visualizations in Tableau or similar BI platforms.

  • Establish data validation, monitoring, and quality assurance best practices.

  • Build APIs and web services enabling self-service data access for engineering teams.

  • Lead automation efforts to improve data operations efficiency.

  • Mentor team members and contribute to continuous improvement initiatives.

Basic Qualifications

  • Education: Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or related technical field. Master’s degree preferred.

  • Experience: 12+ years of experience in data engineering, data platform development, or analytics infrastructure. Proven expertise designing and scaling enterprise data pipelines and complex data architectures. Strong experience working with engineering or telemetry systems data.

  • Technical Skills: Advanced proficiency in SQL, Python, and at least one additional programming language (Java, Scala, or Go). Hands-on experience with ETL/orchestration frameworks (Airflow, dbt, or Prefect). Expertise in modern data warehousing technologies (Snowflake, BigQuery, Redshift, or Azure Synapse). Proficiency in Tableau, Power BI, or equivalent BI platforms. Familiarity with REST APIs, data integration pipelines, and cloud data infrastructure (AWS, Azure, or GCP). Working knowledge of machine learning data preparation is a plus.

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Company

Hewlett Packard Enterprise

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