The Data Engineering team at Glu builds core data infrastructure and applications in support of all areas of our business, including our studio teams, user acquisition, monetization and finance. Glu is passionate about maximizing the value that data and analytics can provide to the business and is aggressively investing in new capabilities. Our team covers a lot of ground from data ingestion through to machine learning applications.
We leverage a cutting-edge tech stack to build both batch systems (YARN+Spark/Hive) and stream processing applications (Kinesis/Flink/Spark Streaming/Druid) that operate efficiently at high scale. The ideal candidate has a strong engineering background and has built robust data platforms and pipelines and takes complete ownership of their area of expertise. This is a fantastic opportunity to use your engineering skills to make a material impact on a highly valued analytics platform
You'll most often be:
- Taking ownership of and developing critical new features for our next-generation analytics platform, supporting Glu's worldwide studios and central functions such as marketing and finance.
- Building scalable, accurate and extensible stream processing applications using cutting-edge technology such as Spark Streaming and Apache Flink.
- Implementing complex and highly scalable end-to-end data pipelines, using Elastic Beanstalk, Kinesis, EMR, Spark, Hive, Druid, Cassandra.
- Building data support for our personalization and experimentation efforts, solving problems from statistical test automation to building real-time M/L applications.
And your skills and experience include:
- Bachelor's degree in computer science/mathematics/engineering, or other fields with proven engineering experience.
- Software engineering experience, especially working on back-end data infrastructure.
- Proficiency with at least one of the following languages: Java, Python, Scala.
- Experience with distributed stream processing technologies such as Flink, Spark Streaming and/or Kafka Streams.
- Experience with AWS Ecosystem, especially Kinesis, EMR, Lambda, and Glue.
- Knowledge of NoSQL application data stores i.e. Druid, HBase, Cassandra, DynamoDB, Redis. .
- Experience with high-scale machine learning, I.e. Spark M/L, SageMaker, etc.
- Experience with SQL and SQL-like languages, especially Hive.
- Experience with CI/CD process, testing framework, and containerization technology
- Experience building data-rich web applications, especially with technologies like Angular, Node.js, and Elastic Beanstalk
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