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# AWS Data Engineer (10737-1) San Francisco, CA

- **Company:** EXECUTIVE RECRUITERS
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Airflow, Amazon Web Services, Amazon S3, Cloud Computing, Information Systems, Databases, Continuous Integration, Extract Transform Load (ETL), Data Systems, Monitoring of Systems, Python (Programming Language), Systems Development Life Cycle, Data Streaming, Workflow Management Systems, Datadog, Scripting, Data Storage Technologies, Apache Spark, Electronic Medical Records, Information Technology, AWS Glue, Performance Monitor, AWS Data Analytics, Data Management, Cloudwatch, Data Pipelines, Amazon Redshift
- **Published:** November 2, 2026
- **Apply:** https://www.careerbuilder.com/job-details/aws-data-engineer-10737-1-san-francisco-ca-san-francisco-ca--088f34b5-4bc3-4707-a7a4-507542faaff2

## About the Role

Amazon Web Services (AWS), Amazon Web Services (AWS), Information Technology & Information Systems, Cloud Computing, Python Programming/Scripting Language, Data Management, Scalable System Development, Database Extract Transform and Load (ETL), Electronic Medical Records, Process Management, Experience level: Mid-senior Experience required: 7 Years Education level: Bachelor's degree Job function: Information Technology Industry: Information Technology and Services Pay rate : View hourly payrate Total position: 1 Relocation assistance: No Visa sponsorship eligibility: No
Job Description:

7+ years of experience in Amazon Web Services (AWS) Cloud Computing
Strong background in designing, developing, and maintaining scalable data pipelines for ingestion and processing using Python, Spark, and AWS services
Key Skills & Expertise

## Description

ETL Development: Implement and manage ETL processes using AWS Glue and EMR for both batch and streaming data
Data Storage Solutions: Design and maintain storage solutions using Medallion Architecture in S3 and Redshift
Workflow Orchestration: Monitor and optimize data workflows using Airflow or other orchestration tools
Data Quality: Ensure accuracy, consistency, and integrity throughout the data lifecycle
CI/CD: Implement and understand CI/CD practices for automated data pipeline deployment
System Monitoring: Utilize CloudWatch and Datadog for performance monitoring and reliability
Collaboration: Work closely with cross-functional teams to gather requirements and deliver business-driven data solutions
Communication: Strong ability to communicate project updates and technical details to stakeholders.
Additional Notes