Data Engineer I, Fire TV
Fire TV, Advertising and Appstore is reshaping the way millions of people discover, engage with, and enjoy entertainment every day. The FAA Decision Science organization is looking for a Data Engineer who is excited to build the data infrastructure powering analytics, experimentation, forecasting, and business decision-making for one of Amazon's fastest-growing product families.
This is an excellent opportunity for an engineer who is early in their career and eager to develop strong data engineering fundamentals in a high-impact environment. You will contribute to foundational data systems used by Data Science, Business Intelligence, Product, Finance, Advertising, and Engineering teams to make high-quality business decisions.
You will build and operate data pipelines, data models, and data quality mechanisms supporting Fire TV engagement, lifecycle analytics, Ads monetization, Appstore performance, executive reporting, experimentation, and AI-enabled analytics. The ideal candidate has solid technical foundations, a willingness to learn, an eye for operational reliability, and the ability to work effectively with cross-functional teammates to translate requirements into working data solutions.
Key job responsibilities
Contribute to the design, development, testing, and deployment of data pipelines and datasets within a defined domain, including the data foundations for GenAI and agentic analytics, working alongside senior engineers
Build and maintain ETL/ELT workflows using SQL, Python, and AWS services
Support data infrastructure operations, including monitoring, alerting, and data quality controls
Investigate and resolve data quality issues, escalating complex problems or architectural trade-offs when appropriate
Partner with Data Science, Business Intelligence, Product, and Engineering stakeholders to understand requirements and implement reliable data solutions
Contribute to conversational and self-service analytics data products
Support compliance, privacy, retention, and governance requirements for data you own or contribute to
Clearly document your solutions to ensure ease of use and maintainability by others
Participate in code reviews, design discussions, and team planning
Participate in on-call and product support for business-critical pipelines and datasets
Build and curate datasets and documentation that AI assisted analytics tools consume
Use AI-assisted development tools to accelerate how you build, test, and debug pipelines
This is an excellent opportunity for an engineer who is early in their career and eager to develop strong data engineering fundamentals in a high-impact environment. You will contribute to foundational data systems used by Data Science, Business Intelligence, Product, Finance, Advertising, and Engineering teams to make high-quality business decisions.
You will build and operate data pipelines, data models, and data quality mechanisms supporting Fire TV engagement, lifecycle analytics, Ads monetization, Appstore performance, executive reporting, experimentation, and AI-enabled analytics. The ideal candidate has solid technical foundations, a willingness to learn, an eye for operational reliability, and the ability to work effectively with cross-functional teammates to translate requirements into working data solutions.
Key job responsibilities
Contribute to the design, development, testing, and deployment of data pipelines and datasets within a defined domain, including the data foundations for GenAI and agentic analytics, working alongside senior engineers
Build and maintain ETL/ELT workflows using SQL, Python, and AWS services
Support data infrastructure operations, including monitoring, alerting, and data quality controls
Investigate and resolve data quality issues, escalating complex problems or architectural trade-offs when appropriate
Partner with Data Science, Business Intelligence, Product, and Engineering stakeholders to understand requirements and implement reliable data solutions
Contribute to conversational and self-service analytics data products
Support compliance, privacy, retention, and governance requirements for data you own or contribute to
Clearly document your solutions to ensure ease of use and maintainability by others
Participate in code reviews, design discussions, and team planning
Participate in on-call and product support for business-critical pipelines and datasets
Build and curate datasets and documentation that AI assisted analytics tools consume
Use AI-assisted development tools to accelerate how you build, test, and debug pipelines