Staff Software Engineer, PlayStation

• Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience
• 8+ years of experience designing and building large-scale distributed systems, with expertise in real-time data processing, analytics, or data platform engineering
• Strong proficiency in Python, with experience building production-grade distributed systems and data processing applications. Experience with Scala is beneficial for supporting and evolving existing platform components
• Hands-on expertise designing and operating large-scale stream processing applications using Apache Flink, Apache Spark Structured Streaming, or equivalent distributed streaming technologies
• Production experience designing, operating, and optimizing distributed analytics platforms using ClickHouse, Apache Druid, or equivalent high-performance OLAP/analytics databases, including query optimization, data modeling, and performance tuning at scale
• Experience building and consuming event-driven data pipelines using AWS MSK, Apache Kafka, or equivalent messaging platforms, with a focus on developing reliable, scalable data processing applications
• Experience applying AI/ML techniques and modern AI tooling to automate engineering workflows, improve data pipelines, simplify platform operations, and enhance developer productivity
• Strong systems design skills with the ability to evaluate architectural trade-offs across scalability, latency, throughput, consistency, reliability, and cost
• Demonstrated technical leadership through architecture ownership, mentoring engineers, and influencing technical decisions across multiple teams

Desirable

• Design, build, and operate large-scale distributed data systems that power real-time and near real-time analytics,, and data-driven applications across PlayStation
• Lead the architecture and evolution of RTAP’s stream processing and analytics platforms, improving scalability, reliability, performance, and developer productivity
• Own critical platform capabilities end-to-end from architecture and implementation through production operations, performance optimization, reliability, and lifecycle management
• Build and optimize distributed data services using technologies such as Apache Flink, Spark Structured Streaming, ClickHouse, and Apache Druid to support high-throughput, low-latency workloads
• Partner with Data Engineering teams to integrate batch and streaming workloads using modern lakehouse architectures and build interoperable, scalable data platforms
• Build AI-powered capabilities to automate engineering workflows, improve data quality, simplify platform operations, accelerate troubleshooting, and enhance developer productivity
• Establish technical standards and engineering best practices for distributed systems, stream processing, data modeling, query optimization and platform reliability
• Mentor engineers across the organization by reviewing architecture, designs, and code while raising the technical bar through coaching and technical leadership
• Partner with Product, Infrastructure, Security, and Platform Engineering teams to define long-term technical strategy and deliver scalable platform capabilities aligned with business priorities
• Investigate and resolve complex production challenges involving high-throughput, low-latency distributed systems, ensuring high availability and operational excellence
• Participate in the team's production support and on-call rotation, leading incident response, conducting root-cause analysis, and implementing reliability improvements that strengthen the platform over time
• Evaluate emerging technologies and distributed data platforms, recommending investments that improve scalability, reliability, developer productivity, and cost efficiency
• 30 Days: Develop a strong understanding of the platform’s architecture, capabilities, engineering practices, and roadmap.Begin contributing to architecture discussions, feature development, design reviews, and production support.Build relationships within the team as well as across the Data Platform organization
• 60 Days: Take technical ownership of a major platform capability or architectural area
• Identify opportunities to improve scalability, performance, reliability, or developer productivity.Collaborate with Data Engineering, Infrastructure, Product, and other partner teams on cross-platform initiatives
• 90 Days: Lead the design and delivery of key roadmap initiatives for the team..Influence technical direction across teams and mentor engineers on distributed systems and platform engineering best practices.Establish clear architectural recommendations for the next phase of the platform’s evolution
• In 90 days, you will have a detailed understanding of the Real-Time Analytics Platform and its role within PlayStation’s broader data ecosystem. You will be comfortable owning complex distributed systems initiatives, partnering closely with Product and Engineering teams, and driving large-scale solutions from architecture through production delivery