Data Engineer II, Compliance Shared Services, RISC
Technology, Data & Digital · Data, AI & Analytics · Data Engineering
In short
Amazon RISC team is seeking an experienced Data Engineer to design, build, and maintain large-scale data pipelines and infrastructure. This role involves optimizing data architectures, implementing reporting, developing data models, and building enterprise-scale data security solutions.
Responsibilities
- Design, develop, and maintain automated ETL/ELT pipelines with monitoring using Python, Spark, SQL, and AWS services.
- Optimize data warehouse and data lake architectures.
- Implement and support reporting and analytics infrastructure.
- Develop optimized data models and transformations.
- Develop and maintain enterprise-scale data security solutions.
- Maintain data warehouse and data lake metadata and documentation.
- Collaborate with customers and technical teams to gather and implement requirements.
- Build self-service tooling and automation, including GenAI-powered agents.
- Design and operate compliance-critical data validation pipelines.
- Monitor and optimize cluster/compute resource utilization.
- Stay current with emerging technologies and incorporate them into the data ecosystem.
Requirements
- 3+ years of data engineering experience
- 4+ years of SQL experience
- Experience with data modeling, warehousing, and building ETL pipelines
- Familiarity with orchestration patterns such as event-driven architectures (EventBridge, Step Functions, Lambda) and workload/cost optimization techniques.
Desired Qualifications
- Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
- Experience with non-relational databases / data stores
- Experience building or operating GenAI-based tooling or agentic workflows (e.g., using frameworks/platforms like AWS Bedrock, MCP-based agents)
Benefits
- Work-life harmony
- Flexibility as part of our working culture
- Continuous knowledge-sharing, mentorship, and career-advancing resources.
#Data Engineering#Compliance#ETL#AWS#Python#SQL#Spark#GenAI