Staff Data Engineer

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The PlayStation brand falls under Sony Interactive Entertainment, a wholly-owned subsidiary of Sony Group Corporation.

Staff Data Engineer (ICDS)

San Mateo, CA

PlayStation® is for The Players.

With over 150 million customers in 109 countries, Sony Interactive Entertainment (SIE) is committed to using data to understand The Players: our customers, better than anyone else. Using the amazing networked power of PlayStation®5 and our other PlayStation systems, we learn how they use them for gaming, and also how they use the networked services like streamed games, music, video, and how they pay, and how we can improve their customer experience. We work with game developers, marketing, R&D, PlayStation®Store, and many other parts of Sony to constantly improve our customer experience and maximize our business potential.

The ICDS DE team has grown rapidly over recent years in response to an increasing need for the business to leverage data to inform and shape key decisions, and to identify new opportunities. Currently, there are approximately 170 staff within the ICDS DE team, split between London and California, made up of high-quality data professionals covering architecture and platform support, data processing, and technical program management

Due to team expansion, ICDS DE is looking to recruit talented, highly driven individuals who have excelled in previous roles and are looking for a new challenge in a dynamic and rewarding environment.

ICDS delivers data that powers Analytics, Data sciences, Marketing, ML, and multiple other teams across the companies and offices that make up the PlayStation business:  high quality, valuable to the user, accessible, and continuously improving.

ICDS Staff Data Engineer

We are looking for a Staff Data Engineer to join the ICDS Player DE team within the Sony PlayStation environment.

A Staff Data Engineer is required to own and evolve large-scale streaming and batch data platforms, providing technical leadership and architectural direction across the data ecosystem.

Based in our San Mateo offices, the team provides data services for various global business units associated with Sony PlayStation.

Responsibilities

Technical Leadership & Architecture

• Lead the design and evolution of large-scale batch and real-time data platforms using Apache Spark, Flink, and Databricks
• Define streaming and event-driven architectures (low-latency, exactly-once, high-throughput) using Flink and Scala
• Set architectural standards for data reliability, scalability, observability, and cost efficiency
• Own end-to-end system design for mission-critical data products, from ingestion to consumption

Data Pipeline Development

• Build and maintain high-performance data pipelines in Scala and Python for both batch and streaming workloads
• Develop real-time processing jobs (windowing, stateful processing, joins) using Flink
• Optimize Spark jobs for performance, cost, and data correctness on Databricks
• Implement robust data quality checks, schema evolution, and fault tolerance

Platform & Operations Excellence

• Drive best practices for CI/CD, automated testing, and deployment of data pipelines
• Establish monitoring, alerting, and SLAs for data systems
• Lead efforts in capacity planning, performance tuning, and cost optimization
• Partner with SRE / Platform teams on reliability, scaling, and incident response
• Ensure code is fully scalable, maintainable, and performant

Cross-Functional Collaboration

• Work closely with product managers, analytics, ML, and backend teams to translate business requirements into scalable data solutions
• Enable downstream use cases including analytics, reporting, experimentation, and machine learning
• Influence data modeling and contract design to ensure long-term platform sustainability
• Work proactively to address project requirements, and articulate issues and challenges with enough lead time to address project delivery risk
• Requires the ability to work in a global and multi-cultural environment. Team members are in multiple geographies, resulting in time constraints due to time zones differences and a requirement for occasional travel

Mentorship & Influence

• Mentor senior and mid-level data engineers; raise the technical bar across the organization
• Lead technical reviews, design discussions, and post-incident analyses
• Influence data engineering strategy across multiple teams or domains
• Act as a go-to expert for streaming systems, Spark optimization, and distributed data processing

Governance, Security & Compliance

• Ensure data platforms adhere to security, privacy, and compliance requirements
• Define best practices for data access control, lineage, and auditing
• Drive standardization around data formats, schemas, and lifecycle management

Experience

• BS Degree in Engineering or Masters, Computer Science or equivalent experience
• 3-5+ years in a technical leadership or staff-level role (design ownership, mentoring, architecture decisions)
• 10-12+ years of hands-on experience building and operating large-scale data pipelines and platforms
• 10-12+ years of relevant industry experience, including ownership of complex, high-impact data systems used at scale
• 10-12+ years of experience in database development, programming (Scala,Python) design, and analysis
• 10-12+ years of experience with distributed framework like Spark, Flink
• 5-7+ years of experience with streaming services – Flink, Kafka
• 10-12+ years of experience with data and ETL programming (Databricks, Ab Initio)
• 7-10+ years of experience with AWS services (EKS, S3, EC2, Kinesis, DynamoDB, Glue, Deequ, etc)
• 7-10+ years of experience in SQL and a variety of database technologies – Snowflake, Teradata and Oracle.
• Experience in designing and architecting medium to complex systems, well versed with design standards & framework
• Expertise in data modeling for analytics and operational use cases
• Experience with data lakes / lakehouse architectures (e.g., Delta Lake)
• Strong understanding of schema evolution, data versioning, and data governance
• Experience with automation, configuration management, orchestration, enterprise schedulers

Skills Required (essential):

• Recognized as a technical authority in data engineering within one or more domains (batch, streaming, lakehouse, analytics platforms)
• Deep hands-on expertise with Scala, Python, Spark, Flink, and Databricks, applied to production systems at SIE scale
• Strong understanding of distributed systems design, trade-offs, and failure modes
• Ability to independently design complex, ambiguous systems with long-term sustainability in mind
• Demonstrates end-to-end ownership of critical data platforms or services, from design through production operations
• Drives engineering best practices for reliability, performance, cost efficiency, and data correctness
• Anticipates risks and proactively addresses scalability, operational, and data quality concerns
• Holds a high operational bar, ensuring systems meet SLAs and business expectations
• Leads or significantly influences architecture and design decisions across teams or programs
• Sets technical direction and standards for data platforms, pipelines, and tooling
• Evaluates and introduces new technologies when they provide clear business or platform value
• Ensures architectural decisions align with SIE platform, security, and compliance standards
• Works effectively across multiple teams, disciplines, and organizations (product, analytics, ML, platform, finance)
• Translates business and product strategy into scalable data solutions that enable revenue, engagement, and operational insights
• Acts as a trusted technical partner for stakeholders beyond the immediate team
• Mentors’ senior and mid-level engineers; raises the overall technical bar
• Leads design reviews, technical forums, and incident postmortems
• Influences outcomes through technical credibility, data-driven decision-making, and clear communication
• Models SIE engineering values and best practices
• Establishes standards for monitoring, alerting, observability, and on-call readiness
• Drives improvements in CI/CD, automation, and developer productivity for data engineering
• Champions cost-awareness and efficiency across data platforms

 

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