Data Engineer

Summary

Join our team as a Data Engineer and contribute to building and deploying data pipelines that address business and technological challenges. You will be responsible for understanding user needs, collaborating with the Data Architect, developing pipelines using various tools, ensuring quality through testing, and deploying infrastructure. This is a remote position.

Requirements

• Google Cloud Platform: General knowledge of the platform and various services, and at least one year of experience with GCP
• Apache Airflow: At least two years of experience with the Airflow orchestrator, experience with Google Composer is a plus
• Google BigQuery: Extensive experience (at least 2 years) with GBQ, know how to optimize tables and queries, and able to design database architecture. The candidate should know more than the regular creation of tables, and should be aware of trade-offs in developing and deploying some infrastructure versus others
• Terraform: At least one years of experience with Terraform

Responsibilities

• Understand problems from a user perspective and communicate to clearly understand the issue
• Ensure the architecture provided by the Data Architect is clearly understood by yourself
• Communicate with the Data Architect and your peers on the technical solution you’re developing and communicate with the Project Manager in charge of the project you’re working on
• Write and communicate on new or updated interface contracts
• Develop data pipelines based on the defined architecture
• Ensure the regular good practices are applied
• Deploy requested infrastructure, particularly using Terraform
• Make peer reviews and ask to your peers to review your code when merging a new version of the codebase
• Define tests with your project manager, based on the functional and technical requirements of the pipeline you’re developing
• Perform those tests and communicate regularly on the results
• Regularly summarize the results of your tests in a dedicated document
• Present to the Data Architect in charge of the architecture, and the Lead DataOps, the development that was performed through our Deployment Reviews
• Track and communicate on any potential errors in the entire period of active monitoring following a deployment
• Ensure diligent application of deployment process, logging, and monitoring strategy

Preferred Qualifications

• Azure Cloud Platform: A general knowledge of the platform is a plus
• Apache Spark: This is an optional expertise we would highly value. Some of our pipelines are slowly being rewritten using pySpark, the Data Engineer should be able to maintain get them evolving
• Pub/Sub
• Kafka
• Google Cloud Storage
• Dataflow (or Apache Beam)