Senior Data Engineer


Job Overview


Global Talent Management (GTM) is centrally responsible for creating and evolving Amazon’s talent programs and processes. Analytics is a growing start-up team inside of GTM with a direct impact on 300,000+ Amazonians across all of our businesses and locations around the world. We regularly use data to pitch ideas and drive conversations with Amazon’s Senior Vice President of PXT (People eXperience and Technology) and other executives about how to improve existing talent programs like Career Development and the Annual Review or invent new ones that address the evolving needs of our diverse employee base.

GTM is looking for Data Engineers to help build the next generation data lake and analytics platform that enables reporting, metrics, analysis, science, and thought-partnership on talent management data for the GTM organization and its stakeholders including senior leadership.

Our Data Engineers build and maintain the GTM data lake infrastructure using best Amazon practices in software engineering, data management, data storage, data compute, and distributed systems. We are passionate about solving business problems with data!

Job duties include the following:

· Develop and support ETL pipelines with robust monitoring and alarming.
· Develop data models that are optimized for business understandability and usability.
· Develop and optimize data tables using best practices for partitioning, compression, parallelization, etc.
· Develop and maintain metadata, data catalog, documentation, and data governance for internal business customers. Help internal business customers troubleshoot and optimize SQL and ETL solutions to solve reporting and analytics problems.
· Work with internal business customers and software development teams to gather and document requirements for data publishing and consumption.


· Bachelor’s degree in Computer Science, Info Systems, Business, or related field.
· 5+ years of Data Engineering experience including one or more query languages (e.g. SQL), schema definition languages (e.g. DDL), ETL tools, and scripting languages (e.g. Python) to build data solutions.
· Track record delivering large scale data solutions (e.g., data model, data architecture, data flow design, tool) in difficult or ambiguous data areas (new or existing systems).


· Builds data solutions that are easy for others to contribute to. Knows how to make data auditable, available, and accessible.
· Has significant knowledge/experience with data design approaches and industry technologies.
· Is able to evaluate end-to-end data designs for strengths and weaknesses (data quality, scalability, latency, security, performance, data integrity, etc.).
· Can anticipate data access patterns and remove bottlenecks. Asks the right questions and drives the right technical solution(s).
· Is able to split development work into parallel tasks that can be performed by them and others and reassembled successfully.
· Is able to influence team technical strategy. Understands that not all problems are new (or require new data solutions). Is able to make appropriate architectural trade-offs (e.g. Build or buy a Business Intelligence technology? Tiered storage strategy?). Shows good judgment when making technical trade-offs between short-term technology needs and long-term business needs
· Can take ownership of a team’s data architecture and make it simpler. Has proactively fixed an architecture deficiency. Resolves root cause.
· Is able to drive data engineering best practices (e.g. Data Discovery, Naming Conventions, Operational Excellence, and Data Security) and set standards.
· Understands system limitations, scaling factors, boundary conditions, and/or the reasons for architectural decisions
Amazon is committed to a diverse and inclusive workplace.

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