Data Engineer II
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.
