Staff Software Engineer – Data

Sony Interactive Entertainment is hiring a Staff Software Engineer – Data to provide technical leadership for its Real-Time Analytics Platform (RTAP). The role designs, builds, and operates distributed data systems and stream-processing platforms, while shaping architecture, mentoring engineers, and improving reliability and scalability across PlayStation.

Responsibilities:

• 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

Requirements:

• 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
• 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
• Experience with Scala is beneficial for supporting and evolving existing platform components
• Experience with Databricks, Apache Iceberg, or modern lakehouse architectures, with the ability to design and integrate batch and streaming workloads into scalable, production-grade data platforms
• Experience building or operating multi-tenant, self-service data platforms supporting multiple engineering organizations
• Experience collaborating with globally distributed engineering teams across multiple organizations and time zones to deliver shared platform capabilities and drive technical alignment