AWS Data Engineer - Redwood Materials

AWS Data Engineer 

Redwood Materials is looking for motivated and talented data engineers to help model and manage data assets in a data lake architecture, overseeing the full data lifecycle from ingestion to processing to consumption. The ideal candidate is someone experienced with both data engineering and creating/managing/supporting AWS infrastructure. This is an opportunity to join during a critical growth phase and build green field software experiences and capabilities which will have a significant impact on the company’s day-to-day operations and ability to scale. 

 Responsibilities will include: 

• Build and manage a data lake in AWS leveraging and augmenting existing LakeFormation based architecture. 
• Build and maintain data pipelines from a variety of data sources, including streaming datasets, APIs, and various data stores, leveraging PySpark and AWS Glue. 
• Create data sets from the data lake to support various use cases, such as business analytics, dashboards, reports and machine learning. 
• Drive technical decisions on the best ways to serve data consumers (data warehouse vs. search engine databases, etc.).
• Leverage existing AWS architectures and design new ones where needed, using the CDK tool kit. 
• Operationalize data workloads in AWS, automating pipelines and implementing appropriate monitoring. 
• Work with cross functional teams to discover business needs and design appropriate data flows. 

 Desired qualifications:  

• Bachelor’s degree in computer science, similar technical field of study, or equivalent practical experience.
• Minimum 3 years of hands-on experience developing data solutions in a modern cloud environment. 
• Fluency in Python.
• Experience authoring and maintaining ETL jobs (PySpark experience a plus).
• Experience designing and interacting with relational and non-relational data stores.
• Experience with AWS ecosystem and resources and using Infrastructure-as-code methodologies (CDK a plus).
• Demonstrated ability to manage production data workloads (detecting and diagnosing issues, monitoring).
• A passion for excellence and making the world a better place!