Data Warehousing Specialist

Fox Corporation•Los Angeles, CA

2d•$183,851 - $202,236•Hybrid

About The Position

As part of the Data Platforms team, Fox Cable Network Services, LLC seeks a Data Warehousing Specialist (Senior Data Engineer) to play an essential role in building robust, scalable, efficient and high-quality Data Engineering solutions using the latest tech stack in a fast-paced environment. Telecommuting is permitted up to 25%/week within normal commuting distance of Fox office in Los Angeles, CA.

Requirements

Bachelor’s degree in Computer Science, Computer Engineering, Data Science, Mathematics, Statistics, Engineering or related field.
Five (5) years of progressive, post-baccalaureate data engineering experience or related experience in architecting, designing and building data engineering solutions and data platforms.
Experience building data engineering pipelines using Python and PySpark.
Experience building Lakehouse or batch/streaming data pipelines in Databricks.

Nice To Haves

Data warehousing on Snowflake
Data acquisition and transformation tools such as Fivetran and dbt
Real-time data stream processing using Apache Kafka, Kinesis, or Spark structured streaming
Distributed data processing frameworks such as Apache Hadoop, Apache Spark, or Flink
Automated workflow scheduling and orchestration tools such as Astronomer or Apache Airflow
Various AWS services including S3, EC2, EMR, Lambda, RDS, Redshift, and Glue Catalog

Responsibilities

Collaborate with and across Agile teams to design and develop data engineering solutions.
Build distributed, low latency, reliable data pipelines ensuring high availability and timely delivery of data.
Design and develop highly optimized data engineering solutions for Big Data workloads to efficiently handle continuous increase in data volume and complexity.
Build highly performing real-time data ingestion solutions for streaming workloads.
Adhere to best practices and agreed upon design patterns across all Data Engineering solutions.
Ensure the code is elegantly designed, efficiently coded and effectively tuned for performance.
Focus on data quality and consistency, implement processes and systems to monitor data quality, ensuring production data is always accurate and available for key stakeholders and business processes that depend on it.
Create design (Data Flow Diagrams, Technical Design Specs, Source to Target Mapping documents) and test (unit/integration tests) documentation.
Perform data analysis required to troubleshoot data related issues and assist in the resolution of data issues.
Focus on end-to-end automation of data engineering pipelines and data validations (audit, balance controls) without any manual intervention.
Focus on data security and privacy by implementing proper access controls, key management and encryption techniques.
Take a proactive approach in learning new technologies, stay on top of tech trends, experimenting with new tools and technologies and educate other team members.
Collaborate with analytics and business teams to improve data models that feed business intelligence tools, increasing data accessibility and fostering data-driven decision making across the organization.
Communicate clearly and effectively to technical and non-technical leadership.