The Data Engineer - Applied Modeling & Data Science on the spoken language understanding (SLU) anlaytics product team is responsible for supporting the data pipeline & engineering needs for the analytics product. The Data Engineer will build and optimize logical data model and data pipelines for difficult datasets in the Alexa SLU analytics product(s), accountable for ongoing data quality, efficiency, testing, and maintenance. The Data Engineer should thrive and have demonstrated success in an environment which offers ambiguously defined problems, big challenges, and quick changes. They will influence mid-size data solutions/access to dataset(s) in team architecture, advising product managers, program managers, and other engineers.
We are looking for passionate data engineers to optimize the consumption of very large data sources we require to generate unique insights. As a data engineering leader within Alexa, we look to you for design, implementation, and successful delivery of large-scale, critical, or difficult data solutions involving a significant amount of work. You will share in the ownership of the technical vision and direction for advanced analytics and insight products. You will be a part of a team of top technical professionals developing complex systems at scale and with a focus on sustained operational excellence. Where needed, you integrate your team's data solutions with those owned by other teams. You influence your team's technical and business strategy by making insightful contributions to team priorities and overall data approach. You take the lead in identifying and solving ambiguous problems, architecture deficiencies, or areas where your team bottlenecks the innovations of other teams. You make data solutions simpler. We are looking for people who are motivated by thinking big, moving fast, and changing the way customers use data to drive profitability. If you love to implement solutions to hard problems while working hard, having fun, and making history, this may be the opportunity for you.
The Data Engineer:
* Has knowledge of recent advances in distributed systems (e.g. MapReduce, MPP architectures, and NoSQL databases). You are proficient in a broad range of data design approaches and know when it is appropriate to use them (and when it is not). * Knowledge of engineering and operational excellence best practices. Can make enhancements that improve data processes (e.g., data auditing solutions, management of manually maintained tables, automating, ad-hoc or manual operation steps). * Works with engineers to develop efficient data querying and modeling infrastructure. * Understands how to make appropriate data trade-offs. Can balance customer requirements with technology requirements. Knows when to re-use code. Is judicious about introducing dependencies. * Writing code that a Data Engineer or Software Development Engineer unfamiliar with the system can understand. * Can create coherent Logical Data Models that drive physical design. * Delivers pragmatic solutions. You do things with the proper level of complexity the first time (or at least minimize incidental complexity). * Understands how to be efficient with resource usage (e.g., system hardware, data storage, query optimization, AWS infrastructure etc.) * Collaboration with colleagues from multidisciplinary science, engineering and business backgrounds. * Communicate proposals and results in a clear manner backed by data and coupled with actionable conclusions to drive business decisions
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