This Data Engineering position is responsible for supporting the data pipelines & engineering needs for Alexa analytics and Machine Learning (ML) products. This Data Engineer will build and optimize logical data model and data pipelines for large, complex, datasets across a variety of Alexa analytics product(s), as well as be 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 large-size data solutions/access to dataset(s) within our team's architecture, advising product managers, program managers, and other engineers on trade-offs and courses of action.
We are looking for a passionate data engineer to optimize the consumption of these 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.

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 we are looking for:

- 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

About the team
Our team owns a self-service analytics platform used by Alexa & AGI developers to identify trends in Alexa performance, assess customer impact, and troubleshoot failure patterns. Our customers include: scientists building and debugging ML models, analysts and researchers measuring the customer experience, developers troubleshooting defects and failures, and data teams building business reports and visualizing metrics.

- 3+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- Knowledge of distributed systems as it pertains to data storage and computing

- Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
- Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $118,900/year in our lowest geographic market up to $205,600/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit This position will remain posted until filled. Applicants should apply via our internal or external career site.