Amazon
Data Engineer II, Managed Operations Engineering & Data Science
Amazon, Herndon, Virginia, United States, 22070
Join our innovative team as a Data Engineer II in Managed Operations Engineering & Data Science (MOEDS). This is your chance to be part of a cutting-edge lab within Amazon Web Services (AWS) that is revolutionizing operational efficiency through advanced data science and engineering.
In this role, you will:
Collaborate:
Work closely with business and software teams to understand their needs and inform system design for operational processes. Data Modeling:
Create robust data models and architectures to promote data-driven initiatives, ensuring high data quality, consistency, and accessibility. Pipeline Development:
Design, build, and maintain efficient and scalable data pipelines to process data from diverse sources into a unified platform. Optimize Performance:
Implement scalable data solutions that support increasing data volumes and enhance data access and querying capabilities. Documentation:
Develop and maintain comprehensive technical documentation to support continuous improvement. As part of our mission in this state-of-the-art innovation lab, you'll leverage cutting-edge technology such as AWS Glue and Apache Airflow, optimizing processes that impact millions of AWS customers globally. We value a culture of inclusion and encourage curiosity and knowledge-sharing among team members. We understand the importance of work-life balance and offer flexibility to support your success both at work and at home. Basic qualifications include: 3+ years of data engineering experience. Experience in data modeling, warehousing, and building ETL pipelines. Knowledge of distributed systems related to data storage and computing. Proficiency in a modern scripting or programming language such as Python, Java, Scala, or NodeJS. Preferred qualifications: 5+ years of data engineering experience. Experience with high availability in distributed systems for data extraction and processing. Familiarity with non-relational databases and AWS technologies such as Redshift, S3, Glue, and Lambda. Embrace the opportunity to shape the future of cloud operations and be part of a diverse, supportive, and innovative team.
Work closely with business and software teams to understand their needs and inform system design for operational processes. Data Modeling:
Create robust data models and architectures to promote data-driven initiatives, ensuring high data quality, consistency, and accessibility. Pipeline Development:
Design, build, and maintain efficient and scalable data pipelines to process data from diverse sources into a unified platform. Optimize Performance:
Implement scalable data solutions that support increasing data volumes and enhance data access and querying capabilities. Documentation:
Develop and maintain comprehensive technical documentation to support continuous improvement. As part of our mission in this state-of-the-art innovation lab, you'll leverage cutting-edge technology such as AWS Glue and Apache Airflow, optimizing processes that impact millions of AWS customers globally. We value a culture of inclusion and encourage curiosity and knowledge-sharing among team members. We understand the importance of work-life balance and offer flexibility to support your success both at work and at home. Basic qualifications include: 3+ years of data engineering experience. Experience in data modeling, warehousing, and building ETL pipelines. Knowledge of distributed systems related to data storage and computing. Proficiency in a modern scripting or programming language such as Python, Java, Scala, or NodeJS. Preferred qualifications: 5+ years of data engineering experience. Experience with high availability in distributed systems for data extraction and processing. Familiarity with non-relational databases and AWS technologies such as Redshift, S3, Glue, and Lambda. Embrace the opportunity to shape the future of cloud operations and be part of a diverse, supportive, and innovative team.