Description
Electronic Arts creates next-level entertainment experiences that inspire players and fans around the world. Here, everyone is part of the story, part of a community that connects people all over the world, where creativity thrives, new perspectives are welcome, and ideas matter.
As a Data & AI Engineer, you will be responsible for designing and delivering 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.
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.
- Map existing data sources with physical and logical architectures to provide service hosting details and infrastructure insights.
- 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.