Data Engineer II (Remote, US)

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Requirements:

• 1 to 2 years of data engineering and data management experience.
• Scripting skills in one or more of the following: Python.
• Basic understanding and usage of a development and deployment lifecycle, automated code deployments (CI/CD), code repositories, and code management.
• Experience with Google Cloud data store and data orchestration technologies and concepts.
• Hands-on experience and understanding of the entire data pipeline architecture: Data replication tools, staging data, data transformation, data movement, and cloud based data platforms.
• Understanding of a modern next generation data warehouse platform, such as the Lakehouse and multi-data layered warehouse.
• Proficiency with SQL optimization and development.
• Ability to understand data architecture and modeling as it relates to business goals and objectives.
• Ability to gain an understanding of data requirements, translate them into source to target data mappings, and build a working solution.
• Experience with terraform preferred but not required.

Responsibilities:

• Design, create, and maintain data solutions. This includes data pipelines and data structures.
• Work with data users, data science, and business intelligence personnel, to create data solutions to be used in various projects.
• Translating concepts to code to enhance our data management frameworks and services to strive towards providing a high quality data product to our data users.
• Collaborate with our product, operations, and technology teams to develop and deploy new solutions related to data architecture and data pipelines to enable a best-in-class product for our data users.
• Collaborating with teammates to derive design and solution decisions related to architecture, operations, deployment techniques, technologies, policies, processes, etc.
• Participate in domain, stand ups, weekly 1:1's, team collaborations, and biweekly retros
• Assist in educating others on different aspects of data (e.g. data management best practices, data pipelining best practices, SQL tuning)
• Build and share your knowledge within the data engineer team and with others in the company (e.g. tech all-hands, tech learning hour, domain meetings, code sync meetings, etc.)