Description:

Electronic Arts is hiring a Data Engineer II for its Data & Insights team based out of the Global Publishing division in Hyderabad. In this position, you will build data structures to support game analytics, player insights, and AI capabilities across the Asian market. The role bridges core data warehousing with modern MLOps architectures, requiring you to construct resilient data pipelines, optimize massive storage footprints, and implement automated data quality safeguards. You will work directly with Data Scientists and Analytics leaders to feed machine learning model registries, optimize vector databases, and implement next-generation AI utilities inside serverless data platforms.

Key Technologies:

Python, SQL, dbt, Snowflake, Airflow, Snowflake Tasks, AWS S3, Apache Iceberg, GitLab CI/CD, Docker

Requirements:

• 3+ years of professional engineering experience designing, deploying, and maintaining high-throughput data integration frameworks and data pipelines.
• Hands-on operational proficiency inside the Snowflake ecosystem (or a matching cloud warehouse engine), including advanced Data Definition Language (DDL), query performance tuning, and the deployment of secure stored procedures.
• Expert-level command over Python and SQL for pipeline automation, paired with active execution experience using dbt (data build tool) for engineering structured data transformation layers.
• Direct experience setting up, monitoring, and debugging data automation workflows utilizing Apache Airflow or native Snowflake Tasks, supplemented by automated data quality frameworks.
• Functional mastery handling large-scale unstructured storage landing zones on AWS S3 alongside working experience provisioning open storage layers like Apache Iceberg tables.
• Solid day-to-day command over Git-based version control, designing pipeline deployments via GitLab CI/CD, configuring environment variables in YAML/JSON, and containerizing services using Docker.
• Baseline exposure to advanced data science infrastructure—specifically supplying feature stores, loading vector databases, provisioning model registries, and exploring Snowflake Cortex features or Pydantic-based RAG (Retrieval-Augmented Generation) systems.
• Familiarity with modern data mesh architectures and data-as-a-product governance metrics, including implementing column masking, object tagging, access control policies, and logging data lineage in catalogs like Alation or Confluence.
• Strong analytical problem-solving habits to weigh architectural trade-offs, combined with clear verbal and written communication patterns to explain complex database updates to cross-functional global partners.