Sr Data Engineer
Experience: 7 25 Years
Location: USA Glendale
MustHave:
Airflow
Apache Spark
Snowflake OR Databricks
Data Modeling
Location: Glendale CA Onsite 34 days a week
The Company
Headquartered in Los Angeles this leader in the Entertainment & Media space is focused on delivering worldclass stories and experiences to its global audience. To offer the best entertainment experiences their technology teams focus on continued innovation and utilization of cutting edge technology.
Platform / Stack
You will work with technologies that include Python AWS Snowflake Databricks and Airflow.
Qualifications:
You could be a great fit if you have:
- 7 years of data engineering experience developing large data pipelines
- Proficiency in at least one major programming language (e.g. Python Java Scala)
- Handson production environment experience with distributed processing systems such as Spark
- Handson production experience with data pipeline orchestration systems such as Airflow for creating and maintaining data pipelines
- Experience with at least one major Massively Parallel Processing (MPP) or cloud database technology (Snowflake Databricks Big Query).
- Experience in developing APIs with GraphQL
- Advance understanding of OLTP vs OLAP environments
- Graph Database experience a plus
- Realtime Event Streaming experience a plus
What Youll Do As a Sr Data Engineer:
- Contribute to maintaining updating and expanding existing Core Data platform data pipelines
- Build and maintain APIs to expose data to downstream applications
- Develop realtime streaming data pipelines
- Tech stack includes Airflow Spark Databricks Delta Lake and Snowflake
- Collaborate with product managers architects and other engineers to drive the success of the Core Data platform
- Contribute to developing and documenting both internal and external standards and best practices for pipeline configurations naming conventions and more
- Ensure high operational efficiency and quality of the Core Data platform datasets to ensure our solutions meet SLAs and project reliability and accuracy to all our stakeholders (Engineering Data Science Operations and Analytics teams)
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