Electronic Arts

As a Data & AI Engineer, you will be responsible for the design and delivery of foundational data services, pipelines, and analytics systems that give visibility into our most important and critical systems - helping gamers to play at scale and having an impact across hundreds of games and millions of gamers. You will also build and leverage AI agents and LLM-powered workflows to automate data engineering operations, enforce data quality, and deliver actionable insights through clear visualizations. You will report to a Development Manager in PI&E.

In your role you will:

• Collaborate with product, program and project management to ensure clarity and understanding of features and priorities.

• Build and maintain pipelines and ingest operational data and metrics from across EA's infrastructure.

• Mapping existing data sources with physical and logical architectures need to provide service hosting details and infrastructure insights

• Experience with and knowledge of LLMs, and agentic workflows

• Design and deploy AI agentic workflows to automate repetitive data engineering tasks such as schema inference, pipeline scaffolding, anomaly triage, and incident summarization.

• Integrate LLMs into operational tooling to enable natural-language querying of infrastructure metrics and automated root-cause analysis.

• Build interactive dashboards and visualizations that translate infrastructure telemetry into clear, actionable insights for engineering and leadership audiences.

The next great EA Engineer Data & AI Engineer also needs:

• Experience using database technologies such as MySQL, MongoDB or Cassandra

• Experience with data lakehouse architectures, storage formats (Parquet, Iceberg, Avro), and OLAP data stores/data warehouses (BigQuery/BigLake, DeltaLake, Snowflake, or Redshift)

• Experience with workflow / ETL management platforms such as Airflow

• Experience with programming languages such as Python, Java, and/or Go.

• Public cloud provider experience (AWS, GCP, Azure.)

• Experience with LLMs and agentic AI frameworks such as LangChain, LangGraph, or Google ADK.

• Hands-on experience using LLMs and agentic developer tools (e.g., Claude Code, GitHub Copilot) to accelerate the software development lifecycle (SDLC) and automate coding tasks.

• Experience with data visualization tools such as Looker, Streamlit or Gradio.

• B.S. in Computer Science or equivalent training.