About the role
Who you are
- 5+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- Experience with SQL
- Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
- Experience mentoring team members on best practices
- Experience building data products incrementally and integrating and managing data sets from multiple sources
- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
- Experience operating large data warehouses
- Experience communicating with users, other technical teams, and management to collect requirements, describe data modeling decisions and data engineering strategy
What the job involves
- As a Data Engineer on this team, you will built data pipeline and analytics solutions for our internal customers to answer question with data, drive critical business decisions and product quality improvements for our customers
- Our Data Engineers use best practices in software engineering, data management, data storage and compute
- You will work on a cross-functional team with other data engineers and scientists, and drive changes that directly impact our customers
- Architect and develop robust data pipelines that ingest and transform data for business intelligence analytics
- Maintain data pipelines using scripting languages such as Python, Spark, SQL and AWS services such as S3, Glue, Lambda, SNS, SQS, KMS
- Design and implement scalable data infrastructure supporting Redshift clusters and visualization dashboards that serve product stakeholders
- Build self-serve data platforms to enable scientists and business stakeholders to answer business questions with data. Implement and support reporting and analytics infrastructure for internal customers
- Ensure compliance with data governance. Address data access restrictions while maintaining adherence to data protection policies and security standards
- Implement data quality monitoring mechanisms with alerting for pipeline failures or anomalies. Ensure data and reporting consistency across the organization
- Excel at communicating complex ideas to technical and non-technical audiences
- Build relationships with product and engineering stakeholders and counterparts
- Work with stakeholders to gather requirements, and translate business needs into data engineering solutions
- Work cross-functionally across product and engineering teams to drive adoption of data assets
- Drive automation and operational excellence in data infrastructure. Reduce manual intervention and improve system reliability
- Scale existing solutions. Create new solutions as required based on team and stakeholder needs
About Amazon
Amazon is guided by four principles: customer obsession rather than competitor focus, passion for invention, commitment to operational excellence, and long-term thinking. We are driven by the excitement of building technologies, inventing products, and providing services that change lives. We embrace new ways of doing things, make decisions quickly, and are not afraid to fail. We have the scope and capabilities of a large company, and the spirit and heart of a small one.
Together, Amazonians research and develop new technologies from Amazon Web Services to Alexa on behalf of our customers: shoppers, sellers, content creators, and developers around the world.
Our mission is to be Earth's most customer-centric company. Our actions, goals, projects, programs, and inventions begin and end with the customer top of mind.
You'll also hear us say that at Amazon, it's always "Day 1." What do we mean? That our approach remains the same as it was on Amazon's very first day - to make smart, fast decisions, stay nimble, invent, and focus on delighting our customers.
About the role
Who you are
- 5+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- Experience with SQL
- Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
- Experience mentoring team members on best practices
- Experience building data products incrementally and integrating and managing data sets from multiple sources
- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
- Experience operating large data warehouses
- Experience communicating with users, other technical teams, and management to collect requirements, describe data modeling decisions and data engineering strategy
What the job involves
- As a Data Engineer on this team, you will built data pipeline and analytics solutions for our internal customers to answer question with data, drive critical business decisions and product quality improvements for our customers
- Our Data Engineers use best practices in software engineering, data management, data storage and compute
- You will work on a cross-functional team with other data engineers and scientists, and drive changes that directly impact our customers
- Architect and develop robust data pipelines that ingest and transform data for business intelligence analytics
- Maintain data pipelines using scripting languages such as Python, Spark, SQL and AWS services such as S3, Glue, Lambda, SNS, SQS, KMS
- Design and implement scalable data infrastructure supporting Redshift clusters and visualization dashboards that serve product stakeholders
- Build self-serve data platforms to enable scientists and business stakeholders to answer business questions with data. Implement and support reporting and analytics infrastructure for internal customers
- Ensure compliance with data governance. Address data access restrictions while maintaining adherence to data protection policies and security standards
- Implement data quality monitoring mechanisms with alerting for pipeline failures or anomalies. Ensure data and reporting consistency across the organization
- Excel at communicating complex ideas to technical and non-technical audiences
- Build relationships with product and engineering stakeholders and counterparts
- Work with stakeholders to gather requirements, and translate business needs into data engineering solutions
- Work cross-functionally across product and engineering teams to drive adoption of data assets
- Drive automation and operational excellence in data infrastructure. Reduce manual intervention and improve system reliability
- Scale existing solutions. Create new solutions as required based on team and stakeholder needs
About Amazon
Amazon is guided by four principles: customer obsession rather than competitor focus, passion for invention, commitment to operational excellence, and long-term thinking. We are driven by the excitement of building technologies, inventing products, and providing services that change lives. We embrace new ways of doing things, make decisions quickly, and are not afraid to fail. We have the scope and capabilities of a large company, and the spirit and heart of a small one.
Together, Amazonians research and develop new technologies from Amazon Web Services to Alexa on behalf of our customers: shoppers, sellers, content creators, and developers around the world.
Our mission is to be Earth's most customer-centric company. Our actions, goals, projects, programs, and inventions begin and end with the customer top of mind.
You'll also hear us say that at Amazon, it's always "Day 1." What do we mean? That our approach remains the same as it was on Amazon's very first day - to make smart, fast decisions, stay nimble, invent, and focus on delighting our customers.