We are seeking a Senior Data Engineer based in Glendale, CA, to play a pivotal role in building and maintaining a robust, scalable, and high-performance Core Data platform. This position requires a hands-on expert who thrives in a dynamic, innovation-driven environment and contributes to the development of real-time and batch data pipelines using modern cloud-native technologies. You will collaborate closely with product managers, architects, and cross-functional engineering teams to deliver reliable, high-quality data solutions that power critical business functions across Engineering, Data Science, Analytics, and Operations. Your work will directly impact the integrity, performance, and scalability of our data infrastructure, ensuring adherence to SLAs and industry best practices. The role is onsite four days per week, offering the opportunity to be deeply embedded in a collaborative, high-impact technical culture.

Contract Terms: 12 Monhts
Working Hours: 8 hours
Working Hours per wk: 40

Responsibilities:

Design, build, and maintain scalable data pipelines using Python, AWS, Spark, Databricks, and Airflow
Develop and manage real-time streaming data pipelines leveraging Delta Lake and other modern data streaming technologies
Build and maintain APIs (including GraphQL) to expose data assets to downstream applications and services
Collaborate with product managers, architects, and engineers to define platform requirements and drive technical execution
Establish and enforce internal and external standards for pipeline configuration, naming conventions, and data governance
Ensure operational excellence, data accuracy, and reliability across all datasets to meet SLAs and stakeholder expectations
Contribute to documentation and continuous improvement of data platform architecture and best practices

Requirements

5+ years of data engineering experience, specifically developing large-scale data pipelines Spark, Airflow, Databricks or Snowflake, SQL, Python
Proficiency in Python, Java, or Scala, with a strong foundation in software engineering principles
Hands-on experience with distributed processing systems such as Apache Spark in production
Proven experience with data pipeline orchestration tools, particularly Apache Airflow
Demonstrated experience with cloud-based MPP databases such as Snowflake, BigQuery, or Databricks
Experience developing APIs using GraphQL or similar technologies
Deep understanding of OLTP vs OLAP systems and their respective use cases
Strong background in distributed data processing, data service software engineering, or data modeling
Must pass a background check required.
Requires onsite presence 4 days per week

Benefits

Three 3 levels of medical insurance for you and your family
Dental insurance (family)
401K
Overtime
California has the following sick leave policy: accrue 1 hour for every 30 hours worked up to 48 hours. If you are based in a different state, please inquire about that state’s sick leave policy.

Learn more about this Employer on their Career Site

Sr Data Engineer

Requirements

Benefits