Security Analytics Engineer

Why Sony Interactive Entertainment?

Sony Interactive Entertainment isn’t just the Best Place to Play — it’s also the Best Place to Work. Sony Interactive Entertainment (SIE) is the company behind the Play Station brand. As a subsidiary of Sony Group Corporation, we’re part of a proud legacy of innovation and excellence. SIE is a dynamic technology company, delivering cutting-edge hardware and network services to more than 100 million people and an entertainment leader, home to some of the most beloved and recognizable intellectual properties (IP) in the world.

Our role at SIE is to create and nurture the experiences under the Play Station brand, a name synonymous with entertainment excellence and creativity.

Job description

The successful candidate will join the Security Data Analytics team, working across Play Station data assets and business groups. This team develops the data products, pipelines, dashboards, and reporting that help Information Security teams understand security posture, risk trends, control effectiveness, exposure, and remediation progress.  Our work is increasingly built on automation and AI-enabled analytics, and this role builds the data foundations that make that possible.

This opening focuses on the backend of that work.  Reporting to the Director of Security Data Analytics, you will design and operate the data engineering foundations our analytics depend on: ingestion and transformation pipelines, Snowflake data models, automation, and the enrichment and AI-ready data patterns that turn raw security signals into trusted, analytics-ready data. Presentation-layer engineers own most visualization delivery, so your primary contribution is the reliability, structure, and performance of the data layer, including keeping it running day to day.

This is a technical, individual contributor role.  You will solve moderately complex problems with limited supervision and own a discrete workstream or a sub-set of a larger effort, working closely with a global set of stakeholders.  This role is UK-based and remote-eligible.

Responsibilities Design, build, and optimize data ingestion, transformation, and enrichment pipelines that turn security, technology, and business data into trusted, analytics-ready data.

Build and maintain Snowflake data models and warehouse objects that support reporting, visualization, automation, and AI-enabled use cases.

Develop automation that reduces manual data handling and reporting effort and improves reliability across the platform.

Prepare AI-ready data and support AI-enabled analytics and reasoning workflows built on our data platform.

Add business and technical context to security data to improve prioritization and decision-making.

Apply software engineering practices to data and analytics code, including version control, testing, code review, and CI.Run and support the team's pipelines and data products in production: monitor health, respond to failures, and troubleshoot and resolve ingestion and pipeline issues to keep data flowing reliably.

Partner with global stakeholders across security, IT, studio, platform, and engineering teams to translate requirements into reliable data and reporting.

Document data models, pipelines, and solutions so the team can support them.

Share knowledge with teammates and contribute to team projects as priorities shift.

Qualifications4+ years creating analytics, reporting, data engineering, or data platform solutions.

Hands-on SQL and strong knowledge of database technologies (relational, columnar, or No

SQL).Hands-on experience with Snowflake, our primary data platform.

Experience building data pipelines and applying data modeling concepts for analytics or data warehouse use cases.

Experience operating data pipelines in production: monitoring, troubleshooting, and resolving ingestion and pipeline failures.

Experience with scripting and automation (Python, Java, R, or similar).Experience preparing data for, or supporting, AI-enabled analytics or machine learning workflows.

Experience with REST APIs, common data formats (JSON, XML), and platform SDKs or APIs.Software engineering practices for analytics code: version control, testing,…