Senior Platform Engineer - Real-Time Data & AI

PlayStation; New York (NY); On-site; USD 170,000 - 230,000; Full time

Job summary

PlayStation is seeking a senior Platform Engineer to design and operate large-scale distributed data systems powering real-time analytics. You will lead architecture for streaming platforms, drive scalability, reliability, and developer productivity across the team.

You will collaborate with Data Engineering, Infrastructure, and Product teams to implement lakehouse architectures, integrate batch and streaming workloads, and mentor engineers across the organization.

Qualifications

• 8+ years of experience designing and building large-scale distributed systems.
• Experience with real-time data processing and analytics platforms.
• Strong coding in Python; knowledge of Scala is a plus.
• Production experience with analytics databases and data platforms.

Responsibilities

• Design, build, and operate large-scale distributed data systems for real-time analytics.
• Lead architecture and evolution of streaming and analytics platforms for scalability and reliability.
• Own platform capabilities end-to-end from design to production operations and maintenance.
• Mentor engineers and raise technical bar through reviews and leadership.
• Collaborate with product, infra, security, and platform teams to align with business priorities and roadmap.

Skills

Distributed systems

Stream processing

Python

Scala (beneficial)

Architecture ownership

Mentoring engineers

Education

Bachelor’s degree in Computer Science or related field

Tools

Apache Flink

Spark Structured Streaming

ClickHouse

Apache Druid

Kafka / AWS MSK

Databricks / Iceberg / lakehouse

Job description

PlayStation is seeking a senior Platform Engineer to design and operate large-scale distributed data systems powering real-time analytics. You will lead architecture for streaming platforms, drive scalability, reliability, and developer productivity across the team.

You will collaborate with Data Engineering, Infrastructure, and Product teams to implement lakehouse architectures, integrate batch and streaming workloads, and mentor engineers across the organization.