Data Engineer, Workforce Solutions - Talent Mobility

- 3+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- 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. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.

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 https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.
Do you love building data pipelines? Are you excited by the opportunity to design tools and infrastructure needed to analyze large volumes of data? Do you want to help solve big data warehousing problems, and partner with stakeholders to understand how to best design and implement cutting edge data solutions that provide answers to key business questions? Do you want to be a part of a fast-paced environment and contribute to one of the most visited sites on the Internet?
At Amazon, we hire the best minds in technology to innovate on behalf of our customers. The intense focus we have on our customers is why we are one of the world’s most beloved brands – customer obsession is part of our company DNA. Data engineers use cutting-edge technology to solve complex problems and get to see the impact of their work first-hand.
The challenges data engineers solve for at Amazon are big and impact millions of customers, sellers, and products around the world. Our path is not always simple, so we are selective about who joins us on this journey. There is a certain kind of person who takes on this role at Amazon – someone who is excited by the idea of creating new products, features, and services from scratch while managing ambiguity and the pace of a company whose ship cycles are measured in weeks, not years.
We’re on the lookout for the curious, those who think big and want to define the world of tomorrow.
At Amazon, you will grow into the high impact, visionary person you know you’re ready to be. Every day will be filled with exciting new challenges, developing new skills, and achieving personal growth.
How often can you say that your work changes the world? At Amazon, you’ll say it often. Join us and define tomorrow.

Key job responsibilities
- Design, implement, and automate deployment of our distributed system for collecting and processing log events from multiple sources.
- Design data schema and operate internal data warehouses and SQL/NoSQL database systems.
- Own the design, development, and maintenance of ongoing metrics, reports, analyses, and dashboards that engineers, analysts, and data scientists use to drive key business decisions.
- Monitor and troubleshoot operational or data issues in the data pipelines.
- Drive architectural plans and implementation for future data storage, reporting, and analytic solutions.
- Develop code based automated data pipelines able to process millions of data points.
- Improve database and data warehouse performance by tuning inefficient queries.
- Work collaboratively with Business Analysts, Data Scientists, and other internal partners to identify opportunities/problems.
- Provide assistance to the team with troubleshooting, researching the root cause, and thoroughly resolving defects in the event of a problem.