Staff Software Engineer, Data Governance & Trust

PlayStation Global•San Mateo, CA

1d•$216,200 - $324,400•Hybrid

About The Position

Sony Interactive Entertainment is seeking a Staff Software Engineer to establish and deliver the technical foundation for Data Governance and Trust across its data and AI ecosystem. The role connects metadata, lineage, data quality, semantic context, and policy controls into practical capabilities that help people, applications, and AI systems use data confidently. It is an individual-contributor leadership role with cross-team impact, combining technical direction and hands-on architecture and implementation. The engineer will shape the architecture and roadmap, lead initiatives from discovery through ongoing operation, build shared services and patterns, and represent the group in discussions with engineering, business, governance, security, and privacy stakeholders. The role also makes governance practical and sustainable by translating requirements into implementable controls, supporting domain teams with contracts and lifecycle practices, and measuring improvements in approved use, adoption, reliability, reuse, and operational effort. The position will help prepare data and context for AI consumption by making business meaning, provenance, quality status, and permitted-use information available through supported interfaces. It also includes mentoring engineers and raising standards for reliability, maintainability, testing, observability, and documentation.

Requirements

Track record of designing, building, and operating production software or data platforms, with demonstrated impact across multiple teams.
Deep practical knowledge of metadata management, data governance, lineage, data quality, privacy engineering, access controls, or closely related platform domains.
Hands-on expertise building or extending metadata and catalog platforms such as Atlan, Alation, DataHub, OpenMetadata, or comparable systems.
Practical understanding of federated ownership models, data stewardship, semantic models, business glossaries, or certified metrics.
Ability to work across warehouse, lakehouse, streaming, and data transformation environments.
Demonstrated delivery of data contracts, automated lineage, quality checks, or policy as code.
Sound judgment translating ambiguous business or policy requirements into clear technical designs and delivered outcomes.
Proven ability to lead initiatives across teams without direct reporting authority.
Skill leading difficult technical or organizational conversations, resolving competing priorities, and securing clear decisions and commitments.
Strong written and verbal communication, including architecture proposals, decision documents, and presentations tailored to different audiences.
Collaborative approach grounded in listening, respectful challenge, and shared accountability.
Commitment to mentoring engineers, supported by examples of improving team effectiveness.

Nice To Haves

Strong software engineering skills, including proficiency in Python, Java, or Go, and practical SQL experience.
Experience with distributed systems, APIs, integrations, and cloud data infrastructure, including sound judgment about reliability, consistency, security, and failure handling.
Experience implementing controls that generate operational evidence.
Experience delivering governed data or context services for analytics, applications, machine learning, or AI agents.
Experience leading platform adoption, modernization, or migration across organizational boundaries.
Experience representing technical work through internal forums, technical writing, communities of practice, or external presentations.

Responsibilities

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 and ambiguous requirements 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.
Remain hands-on through prototypes, production code, design reviews, and solving complex technical problems.
Represent the group in architecture discussions, planning forums, domain engagements, and leadership reviews.
Facilitate stakeholder discussions, surface disagreements constructively, explain tradeoffs and implications, and guide decisions toward clear outcomes.
Document decisions, clarify responsibilities and dependencies, and follow through on commitments across teams.
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, and integration choices based on interoperability, usability, reliability, operating cost, and long-term flexibility.
Measure success through improvements in approved data use, adoption, reliability, reuse, and operational effort.
Make business meaning, provenance, quality status, and permitted-use information available to applications and AI through supported interfaces.
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 semantic definitions and metadata models while preserving accountability within business domains.
Mentor engineers in system design, technical judgment, stakeholder engagement, and leadership.
Raise engineering standards for reliability, maintainability, testing, observability, and documentation, and help engineers take broader ownership.