Data Engineer Jobs

About The Position

Requirements

• Strong knowledge of SQL for complex querying, optimization, and database design.
• Experience with time-series databases, preferably Timestream.
• Hands-on experience in designing, implementing, and managing time-series data in Timestream.
• Proficiency in defining retention policies, querying, and optimizing time-series data workflows.
• Proficiency in Python for data engineering tasks, including integration with client services.
• Experience with Python libraries such as Pandas, SQLAlchemy, or PySpark for handling data processing and analysis.
• Deep knowledge of cloud services related to data engineering such as S3, Lambda, Redshift.
• Experience with managing data pipelines, security, and automation.
• Experience in schema design, performance optimization, and maintenance for both relational and NoSQL databases.
• Understanding of best practices for cloud-based database management, including backup, disaster recovery, and monitoring.
• Strong troubleshooting skills for performance issues in both data pipelines and databases.
• Experience with scaling databases and pipelines to accommodate increasing data volume and complexity.
• Must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum.

Responsibilities

• Designing, implementing, and managing time-series data in Timestream.
• Defining retention policies, querying, and optimizing time-series data workflows.
• Developing Python for data engineering tasks, including integration with client services.
• Handling data processing and analysis using Python libraries such as Pandas, SQLAlchemy, or PySpark.
• Managing data pipelines, security, and automation using cloud services like S3, Lambda, and Redshift.
• Schema design, performance optimization, and maintenance for both relational and NoSQL databases.
• Implementing best practices for cloud-based database management, including backup, disaster recovery, and monitoring.
• Troubleshooting performance issues in data pipelines and databases.
• Scaling databases and pipelines to accommodate increasing data volume and complexity.