Job description
Job details
- Location: Hawthorne, Los Angeles County
- Work mode: Onsite
- Employment type: Full-time (Not an internship)
- Salary: USD 152,813 per year
Role overview
Amazon is hiring a Data Engineer II for the Ring and Blink Customer Service Engineering and Insights team in Hawthorne, Los Angeles County. This mid-level, full-time onsite position focuses on building and maintaining the data platform foundations that power analytics, AI initiatives, and data-driven capabilities across the organization. The role involves hands-on work with ETL pipelines, AWS data infrastructure, and ensuring data security, reliability, and compliance for downstream consumption. The salary for this Data Engineer II role is $152,813 USD.
Job details
This is a full-time, onsite position located in Hawthorne, Los Angeles County, California. The Data Engineer II will work on-site at Amazon's Ring and Blink offices. The role is execution-focused with strong emphasis on orchestration, pipeline optimization, and infrastructure management. Salary is $152,813 USD annually.
Responsibilities
- Design, build, and maintain scalable ETL pipelines for customer service data
- Operate and optimize AWS data infrastructure ensuring security and compliance
- Enable analytics and AI initiatives through reliable data platform foundations
- Orchestrate data workflows and ensure data quality for downstream consumers
- Collaborate with engineering and analytics teams on data-driven solutions
Requirements
- 2-4 years of experience in data engineering or related field
- Strong proficiency in Python and SQL for data processing
- Hands-on experience with AWS data services (S3, Redshift, Glue, EMR)
- Experience building and maintaining ETL pipelines at scale
- Knowledge of data security, compliance, and governance best practices
- Familiarity with orchestration tools like Apache Airflow or similar
Benefits
- Competitive salary of $152,813 USD
- Comprehensive Amazon benefits package
- Work on high-impact Ring and Blink products
- Career growth in data engineering at scale