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We're looking for a Staff AI Data Engineer to design and build the data infrastructure, pipelines, and services that power AI and machine learning workflows across PlayStation Studios. You'll own systems that move, transform, organize, and serve data for LLM applications, agentic workflows, analytics, experimentation, and production AI services.

This is a hands-on Staff-level engineering role spanning data engineering, backend systems, cloud infrastructure, and applied AI. You'll work across the stack to build reliable data platforms and services, establish scalable architectural patterns, and help teams turn studio data into useful, production-ready AI capabilities.

Design, build, and own scalable data infrastructure supporting LLM, agentic, machine learning, analytics, and experimentation workflows.

Build and operate reliable batch and event-driven data pipelines for ingestion, transformation, enrichment, and delivery across varied studio data sources.

Develop ETL/ELT pipelines that feed data warehouses and curated datasets for analysts, analytics engineers, ML engineers, and downstream applications.

Design data models, APIs, and storage patterns that make structured and unstructured data easy to discover, access, and use across AI-powered systems.

Build data-backed services and platform capabilities for LLM applications, including retrieval-augmented generation, embeddings, vector search, tool integrations, and context retrieval.

Develop backend services and reusable components in Python that support production AI and data workflows.

Design and operate cloud infrastructure using AWS and/or GCP, Infrastructure as Code, containerized workloads, and modern CI/CD practices.

Establish standards for data quality, lineage, observability, testing, schema evolution, reliability, security, and operational ownership.

Evaluate new technologies and architectural approaches across data engineering and the rapidly evolving LLM and agent ecosystem, and determine where they provide practical value.

Partner with AI/ML engineers, software engineers, analysts, analytics engineers, researchers, and studio teams to translate ambiguous requirements into scalable technical solutions.

Provide technical leadership across projects, influence architecture and engineering standards, mentor other engineers, and help shape the long-term direction of the AI Engineering data platform.

Produce clear technical documentation, architectural guidance, examples, and reusable patterns that allow solutions to scale across teams and studios.

You have at least seven years of experience building and operating production data platforms, backend systems, or distributed data-intensive applications.

You are highly proficient in Python and have experience building maintainable production software, not just scripts or notebooks.

You have designed and operated production data pipelines, including ingestion, transformation, orchestration, monitoring, failure recovery, and data-quality validation.

You have strong experience with relational databases and analytical data platforms such as PostgreSQL, Redshift, Snowflake, BigQuery, or similar systems.

You are experienced with cloud-native architecture on AWS, GCP, or another major cloud platform and understand networking, security, storage, compute, and managed data services.

You have hands-on experience with Infrastructure as Code such as Terraform and with modern CI/CD and DevOps practices.

You understand data modeling, schema design, query performance, partitioning, data lifecycle management, and the tradeoffs between transactional, analytical, and specialized storage systems.

You have experience designing APIs, services, or other programmatic interfaces for accessing and operating on data.

You are comfortable working in collaborative Git-based development environments with code review, automated testing, and production deployment workflows.

You communicate clearly, write strong technical documentation, and can translate broad or ambiguous problems into pragmatic, maintainable systems.

You operate effectively at Staff level: independently driving architecture and execution, influencing technical direction across teams, and raising engineering standards beyond your immediate projects.

You have built production systems using LLMs, agentic workflows, retrieval-augmented generation, embeddings, or vector databases.

You have experience with LLM application infrastructure and tooling such as MCP, tool calling, evaluation systems, prompt/context management, or model observability.

You have worked with orchestration and distributed data-processing technologies such as Airflow, Prefect, Dagster, Spark, Kafka, or similar systems.

You have experience working with both structured and unstructured data, including documents, source code, telemetry, logs, media metadata, or other large-scale content.

You have built internal data platforms, self-service developer platforms, or reusable infrastructure used by multiple engineering teams.

You are familiar with data governance, privacy, security, access controls, lineage, retention, and responsible-AI considerations for enterprise data.

You have experience supporting ML training, evaluation, feature generation, or other machine learning data workflows.