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지역: Austin, Texas, United States of America 
  • 장소: Austin
  • 시/도:
  • 국가: United States of America


역할 ID
200554
근로자 유형
Regular Employee
스튜디오/부서
CTO - EA Digital Platform
유연근무제
Hybrid

설명 및 참여 요건

당사는 크리에이터, 스토리텔러, 기술자, 경험 생산자, 혁신가 등으로 구성된 글로벌 팀입니다. 당사는 서비스를 제공하는 플레이어만큼이나 다양한 팀에서 놀라운 게임과 경험이 시작된다고 믿습니다. Electronic Arts에서 불가능은 없습니다.

Software Engineer - Data Platform

Electronic Arts, we are a global team of creators, storytellers, technologists, experience originators, innovators and so
much more. We believe amazing games and experiences start with teams as diverse as the players and communities we
serve. At Electronic Arts, the only limit is your imagination.

The EA Digital Platform (EADP) group is the core powering global EA digital ecosystem. We provide the foundation for all
of EA’s incredible games and player experiences with high-level platforms like Cloud, Commerce, Data and AI, Gameplay
Services, Identity and Social. By providing central, reusable capabilities that game teams can easily integrate into and rely
on, we help them focus on making some of the best games in the world and creating meaningful relationships with our
players. We’re behind the curtain, making it all work together. Come power the future of play with us.

The EADP - Data&AI team owns and operates a unified, versatile Data Platform that caters to a variety of needs across
EA such as Data, Analytics, Data Science (ML, AI) and Player Life Cycle Management (LCM). High fidelity Data Pipelines,
Tools and Technologies used to collect, ingest, process and deliver data/insights at multi-petabyte(s) scale across
franchises at Electronic Arts. This modern, cross-cloud stack combines the power of open source and vendor tech to
meet the ever growing Data and AI demand at EA. Our data platform powers the future of game development,
marketing, sales

Responsibilities:
Build, Scale and Operate unified data platform/components that handles data at scale end to end. High fidelity
collection mechanism, Data pipelines that extract and process massive amounts of data, Robust data delivery
channels that deliver data in any cadence (RT, NRT, Mini-batch, Batch etc.) are all part of the massive ecosystem.
Develop Data Engineering infrastructure, software, pipelines on a modern, cloud based distributed data platform
that harnesses the power of open source and vendor technologies such as Spark, Hadoop/MapReduce, Trino,
Kafka, Storm, Flink, Airflow etc.
Develop federated consumption mechanisms such as connectors for raw data access, robust APIs, bulk data
delivery mechanisms to a variety of destinations that serve consumers all across EA (20+ Game studios) to fuel
business insights and intelligence.
Build large-scale repositories that deliver time sensitive data to power LCM (Player Life Cycle Management)
personalize player experience and drive meaningful business growth.
Build data observability stack/tools to detect anomalies, monitor, track and forecast potential areas of failures.
Build quantitative and qualitative KPIs to monitor the business impact of our data platform deliveries.
Have a strong analytics mindset to develop complex queries to solve deep data mining problems.
Build workflows on large scale analytical Data Warehouses like Snowflake, Redshift, Big Query etc.
Collaborate with Product Management effectively to understand customer demand and build disciplined
engineering specs for design and development.
Requirements:
Solid foundation in Computer Science, with competencies in algorithms, data structures, and software design.
Bachelor’s degree in Computer Science writing clean reusable code, test-driven development and CI/CD.
Fluency with an Object-oriented language including Java/C++, Python, SQL.
Fast prototyping skills, familiarity with scripting languages such as bash, awk, or python.
Experience with distributed data platforms/systems serving large concurrent requests is preferred.
Experience working with large-scale analytical systems and data platforms/warehouses is preferred.
Experience with Spark, MapReduce, Hadoop, Hive, Trino or other NoSQL stacks is preferred.
Experience with data modeling and BI tools is preferred.