Associate Data Engineer at Glu Mobile
Sorry, this job was removed at 5:52 a.m. (PST) on Wednesday, April 22, 2020
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:
- Maintaining, streamlining and hardening existing data pipelines, from ingestion, through ETL and batch processing in order to reliably process billions of records per day.
- Build data support for our personalization and experimentation efforts, solving problems from statistical test automation to building real-time M/L applications.
- Working with Analytics and Product Management to ensure optimal data design and efficiency.
- Assisting Data Analysts and Data Scientists with pipeline and model deployment
And your skills and experience include:
- Bachelor's degree in computer science/mathematics/engineering, or other fields with proven engineering experience.
- Professional software engineering experience, especially working on back-end data infrastructure.
- Proficiency with at least one of the following languages: Java, Python, Scala.
- Proficiency with Spark and/or similar tools in Hadoop/YARN environment and comfortable with Linux operating system.
- Proficiency with SQL and SQL-like languages, especially Hive.
- Experience with AWS Ecosystem, especially Kinesis, EMR, Lambda, and Glue.
- Experience with high-scale machine learning, I.e. Spark M/L, Sagemaker, etc.
- Knowledge of NoSQL application data stores i.e. Druid, HBase, Cassandra, DynamoDB, Redis.
Read Full Job Description