| Location | Seattle, WA |
Data Engineer
Hybrid - Seattle, WA
USC, GC or H4 EAD ONLY
As an Data Engineer II, you will bring a high level of technical knowledge, but also an ability to spread knowledge to your co-workers. You will help form the core of our engineering practice at the company by contributing to all areas of development and operations (pre-production to production). You will be an example of what good engineering looks like and help others around you refine their skills. You will be part of a day-to-day production release team and may perform on-call support functions as needed. Having a DevOps mindset is the key to success in this role, as Engineers are commonly part of full DevOps teams that "own" all parts of software development, release pipelines, production monitoring, security and support.
Data Engineering Projects
Data pipeline creation and maintenance. Stack: Google Cloud Platform (GCP), Azure Cloud, Azure Databricks, Snowflake
Create secure data views and publish them to the Enterprise Data Exchange via Snowflake for other teams to consume
Data pipeline modernization and migration via Databricks Delta Live Tables (DLT) and Unity Catalog
Leverage existing CICD process for pipeline deployment
Adhere to PII encryption and masking standards
Data Engineering Tools/Techniques
Orchestration tools- ADF, AirFlow, FiveTran
Languages- SQL, Python
Data Modeling- Star and Snowflake Schema
Streaming- Kafka, EventHub, Spark, Snowflake Streaming
DevOps Support
Support improvements to current CICD process
Production monitoring and failure support
Provide an escalation point and participate in on-call support rotations
Participate in discussions on how to improve DevOps
Be aware of product release and how that impacts our business
Take part in Agile ceremonies
Perform engineering assignments using existing procedures and best practices
Conduct research to aid in product troubleshooting and optimization efforts
Participate in and contribute to our Engineering Community of Practice
Qualifications:
Must haves:
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DevOps, data, GCP), Azure Cloud, Azure databricks, Snowflake •