Principle Data Engineer

Insight Global; San Diego (CA); On-site; USD 110,000 - 170,000; Full time

Job summary

Insight Global is seeking a Data Engineer for a leading semiconductor research organization. You will help shape the data foundation that supports R&D, building data pipelines and enabling access to research data at scale.

You will collaborate with lab owners, experimental teams, ML scientists, and IT to establish architecture standards, governance, security, and scalable data platforms.

Qualifications

• Experience building and optimizing data pipelines.
• Ability to integrate diverse data sources and enable scalable data access.
• Knowledge of data governance, security, and metadata management.
• Collaborative mindset and ability to work with ML scientists and engineers.

Responsibilities

• Define and evolve data architecture standards for Source Research.
• Build and integrate data pipelines connecting research protos, test benches, simulations, and HPC resources.
• Establish data governance, data lineage, access controls, and metadata management.
• Monitor and optimize data pipelines for reliability, performance, and cost.
• Partner with Source Research, Engineering, and IT to align on a common data platform architecture.
• Enable integration of physics-based models, AI workflows, and simulation workflows.
• Document platform architecture and communicate technical concepts to stakeholders.

Job description

Join a leading semiconductor research organization developing next-generation lithography light source technologies. In this role, the Data Engineer will help shape the data foundation that supports research and development activities across Source Research. Working closely with lab owners and experimental, modeling, and ML scientists, they will build and improve data pipelines, integrate diverse data sources, and enable reliable access to research data at scale. They will also help establish practical architecture standards and best practices that ensure data platform remains scalable, secure, maintainable, and aligned with the broader data landscape.

This role combines hands-on development with technical leadership in shaping the data foundation for Source Research. The date engineer will build, operate, and continuously improve data pipelines, integrating new data sources, improving reliability, and enabling scientists and engineers to use high-quality data at scale. They will also define practical architecture standards that keep the platform consistent, secure, future-ready, and aligned with data landscape.

• Define and evolve the data architecture strategy and standards for Source Research to enable data analytics, and Machine Learning workflows.
• Build and integrate data pipelines that connect research source protos, experimental test benches, and simulation, ensuring data is discoverable, accessible, and reusable by scientists and engineers.
• Establish data governance standards and best practices, including data lineage, access control, metadata management, security and lifecycle policies.
• Monitor and optimize data pipelines: implement quality controls and validation rules, track operational health, troubleshoot failures, and improve performance and cost efficiency.
• Partner with teams across Source Research, Engineering, and IT to establish and align on a common data platform architecture.
• Enable integration of physics-based models, AI capabilities, simulation workflows, and high performance computing resources to support system level understanding, analysis and technology development.
• Document platform architecture, design decisions, standards, and best practices, and communicate technical concepts effectively to both technical and non-technical stakeholders.