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# hybrid Data Engineer / Data Platform Engineer

- **Company:** Mozilla Corporation
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Airflow, Amazon Web Services, Microsoft Azure, BigQuery, Information Engineering, Data Infrastructure, Data Structures, Data Flow Control, Python (Programming Language), SQL Databases, Data Processing, Kubernetes, Data Management, Software Coding, Terraform, Looker Analytics, Apache Beam
- **Published:** September 9, 2026
- **Apply:** https://job-boards.greenhouse.io/mozilla/jobs/8180982

## About the Role

* 10+ years of combined experience in Data Engineering, Data Platform Engineering, Data Science, or Data Analyst roles
 * Deep expertise in Data Engineering and Data Platform Engineering.
 * Experience with data tooling in GCP, AWS, or Azure, including Pub/Sub, BigQuery, and Dataflow (Apache Beam), or similar technologies.
 * Experience with Looker, Fivetran, Kubernetes / GKE, Airflow / Cloud Composer, and Terraform, or similar technologies.
 * Advanced coding skills in SQL and Python.
 * Strong software engineering fundamentals: modularity, abstraction, data structures, and algorithms.
 * Experience as a tech lead, team lead, hands-on Data Engineering or Data Platform manager, or similar leadership role.
 * Commitment to our values:

 * Welcoming differences
 * Being relationship-minded
 * Practicing responsible participation
 * Having grit

## Description

* Design, build, and improve systems to reliably and efficiently process data at the scale of tens of terabytes per day, and apply those systems to solve meaningful problems for the business.
 * Evaluate new technologies. Work with Data Engineering and Data Platform Engineering not only to implement these technologies but also to set best-practice standards.
 * Craft and improve workflows in GCP using standard, modern components such as Dataflow, BigQuery, Pub/Sub, GKE, and Airflow.
 * Drive cross-functional projects with stakeholders across Mozilla, including Data Science, Product, Legal, Policy, and Engineering.
 * Mentor teammates and develop engineering best practices across the Data Organization.
 * Work with Data Science to design and implement useful data abstractions.
 * Address the evolving needs of active Data Products.


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