Staff Software Engineer, Data Governance & Trust
Sony Interactive Entertainment Global · United States, San Mateo, CA
About this role
**Staff Software Engineer, Data Governance & Trust** **Location:** San Mateo, CA --- ## About the role Sony Interactive Entertainment (SIE) is seeking a **Staff Software Engineer** to help shape and deliver the technical foundation for **Data Governance and Trust** across our data and AI ecosystem. Your mission is to make data **understandable, reliable, reusable, and appropriately governed**—with shared platform capabilities, clear ownership, and governance practices embedded into how teams build and operate data products. You’ll connect **metadata, lineage, data quality, semantic context, and policy controls** into practical capabilities that help teams use data with confidence. You’ll also serve as a trusted technical representative for the Data Governance and Trust group, leading discussions and building alignment across engineering, business, governance, security, and privacy stakeholders. --- ## What you will do ### Set technical direction and deliver shared capabilities - Shape the architecture and technical roadmap for Data Governance and Trust with engineering leadership, product partners, domain teams, and governance stakeholders. - Lead initiatives from discovery through design, implementation, adoption, and ongoing operation. - Design and build services, integrations, APIs, and automation connecting metadata, ownership, classification, lineage, quality signals, and policy information. - Establish reusable engineering patterns that embed trust requirements into data publishing, discovery, access, consumption, change, and retirement. - Stay hands-on via prototypes, production code, design reviews, and resolution of complex technical problems. ### Lead conversations and build alignment - Serve as a technical spokesperson in architecture discussions, planning forums, domain engagements, and leadership reviews. - Facilitate stakeholder discussions, surface disagreements constructively, and guide decisions to clear outcomes. - Explain technical choices, business implications, risks, and costs to technical and nontechnical audiences. - Challenge assumptions respectfully, incorporate feedback, and help teams reach workable agreements on standards, ownership, and implementation. - Document decisions, clarify responsibilities and dependencies, and follow through on commitments. - Build relationships that enable adoption through credibility, collaboration, and influence. ### Make governance practical and sustainable - Partner with governance, security, privacy, and domain stakeholders to translate requirements into implementable controls and traceable evidence. - Help domain teams define data contracts, quality expectations, ownership responsibilities, and lifecycle practices supported by shared platform capabilities. - Connect quality and reliability signals to accountable owners, consumer communications, and remediation workflows. - Evaluate build/buy/integration choices based on interoperability, usability, reliability, operating cost, and long-term flexibility. - Measure success via improvements in approved data use, adoption, reliability, reuse, and operational effort. ### Prepare the foundation for AI consumption - Develop ways to make business meaning, provenance, quality status, and permitted-use information available to applications and AI. - Partner with AI and platform teams on governed access to data and context, including traceability and defined behavior when trust information is missing. - Support shared semant
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