Staff Software Engineer – Data

PlayStation Global

5h•$198,200 - $297,400

About The Position

Sony Interactive Entertainment is hiring a Staff Software Engineer for its Data Platform team. The role provides technical leadership in designing and evolving the Real-Time Analytics Platform (RTAP), which enables high-throughput, low-latency analytics and stream processing across PlayStation. The engineer will design, build, document, and operate large-scale distributed data platforms for real-time and near-real-time analytics, address distributed-systems challenges, evolve streaming and analytics architecture, integrate AI-powered platform capabilities, and improve scalability, reliability, performance, and developer productivity. The role works closely with Engineering, Product, Infrastructure, Security, and Data Engineering teams to shape technical strategy, influence architecture across organizations, mentor engineers, and guide the long-term evolution of PlayStation’s data platform. Responsibilities span platform architecture and implementation through production operations, performance optimization, reliability, and lifecycle management. The position also participates in production support and an on-call rotation. During the first 30 days, the successful candidate is expected to learn the platform architecture, capabilities, engineering practices, and roadmap; contribute to architecture discussions, feature development, design reviews, and production support; and build relationships across the Data Platform organization. By 60 days, they are expected to take technical ownership of a major platform capability or architectural area and identify improvements. By 90 days, they are expected to lead roadmap initiatives, influence technical direction, mentor engineers, and make architectural recommendations for the platform’s next phase. The role offers an estimated base pay range of $198,200–$297,400 USD. It is eligible for SIE’s benefits package, including medical, dental, vision, matching 401(k), paid time off, a wellness program, and employee discounts for Sony products. The role may also be eligible for a bonus package. SIE conducts background checks at the offer stage for new employees.

Requirements

Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
At least 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 and 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 or 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 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, including evaluating 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.

Nice To Haves

Experience with Scala to support and evolve existing platform components.
Experience with Databricks, Apache Iceberg, or modern lakehouse architectures, including designing and integrating batch and streaming workloads into scalable, production-grade data platforms.
Experience building or operating multi-tenant, self-service data platforms that support multiple engineering organizations.
Experience collaborating with globally distributed engineering teams across organizations and time zones to deliver shared platform capabilities and drive technical alignment.

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 for 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 through architecture, design, and code reviews, 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, supporting high availability and operational excellence.
Participate in production support and the team’s on-call rotation, lead incident response, conduct root-cause analysis, and implement reliability improvements.
Evaluate emerging technologies and distributed data platforms, recommending investments that improve scalability, reliability, developer productivity, and cost efficiency.