Staff Software Engineer – Data

PlayStation Global•San Diego, CA

17h•$198,200 - $297,400•Hybrid

About The Position

Do you want to join a team of talented engineers building the next generation of real-time data platforms that power analytics and data-driven experiences across PlayStation? As part of our Data Platform team, you will play a key technical leadership role in designing and evolving the Real-Time Analytics Platform (RTAP), enabling high-throughput, low-latency analytics and stream processing at PlayStation scale. The Software Engineers within our Platform Engineering Group design, build, document, and operate large-scale distributed data platforms that support real-time and near real-time analytics across PlayStation. You will solve complex distributed systems challenges, evolve our streaming and analytics architecture, integrate AI-powered platform capabilities, and improve scalability, reliability, performance, and developer productivity. We’re looking for someone who is passionate about distributed systems, real-time analytics, and building scalable data platforms. You will work closely with Engineering, Product, Infrastructure, Security, and Data Engineering teams to shape technical strategy, influence architecture across organizations, mentor engineers, and help drive the long-term evolution of PlayStation’s data platform.

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. 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.

Nice To Haves

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.

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.