Senior Data Engineer, ADE Science
Amazon
Description
The Alexa Daily Essentials team delivers experiences critical to how customers interact with Alexa as part of daily life. Alexa users engage with our products across experiences connected to Timers, Alarms, Calendars, Food, and News. Our experiences include critical time saving techniques, ad-supported news audio and video, and in-depth kitchen guidance aimed at serving the needs of the family from sunset to sundown.
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.
Key job responsibilities
Data Infrastructure
- 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
Communication
- 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
Operations
- 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.