Are you passionate about building data engineering platform and using data driven solutions to solve a variety of problems? Do you have a solid background in data engineering, statistics, and analysis? We’re looking for a Data Engineer to join our Perfect Order Experience - Centralized Analytics and Data Engineering team. This is your opportunity to be part of Perfect Order Experience (POE) team at Amazon which is responsible for providing buyers a perfect order experience so they can shop with confidence on Amazon. The team's mission is to ensure buyers receive authentic, non-infringing products in the condition and with the functionality they expect, and to quickly make things right if anything goes wrong. We do this by ensuring that Buyers receive authentic, non-infringing products in the condition and with the functionality they expect, and by giving them the confidence that Amazon stands behind every product and will quickly make it right in the rare chance anything goes wrong. We leverage leading-edge technology and AI to detect, prevent, and remediate risk at scale, collaborate deeply with brands to protect their intellectual property, and empower selling partners to build customer trust and long-term success.
We’re very interested in candidates with a strong data engineering and business intelligence background. Successful candidates will have a proven track record of rapidly solving problems with data, is highly analytical and possess a strong passion for developing insights. You will work with a variety of stakeholders including business and engineering teams across the organization. You should have hands-on knowledge of various AWS technologies revolving around data storage and management. You should love working with data, be driven to dive deep, and socialize business impacts to guide the broader organization. A self-starter with keen attention to detail, ability to work in a fast-paced environment and good track record of influencing decisions. Above all you should be passionate about working with huge data sets and eager to learn new solutions to answer business questions and drive change.
Key job responsibilities Design, build, and maintain an analytical platform providing access to large datasets and computing power. Design, build and maintain data clusters providing access and insights for business needs. Design build and maintain data pipelines to transport relevant data to the right destinations. Implement data structures using best practices in data modeling. Collaborate with Engineering and Science teams to implement advanced analytics algorithms that exploit our rich data sets for statistical analysis, prediction, clustering and machine learning. Recognize and adopt best practices in reporting and analysis: data integrity, test design, analysis, validation, and documentation. Build robust and scalable data integration (ETL) pipelines. Author and evangelize Data handling best practices for the organization. Interface with other technology teams to extract, transform, and load data from a wide variety of data sources. Continually improve ongoing reporting and analysis processes, automating or simplifying self-service support for customers.
About the team CADE’s mission is to bring together best data engineering practices, BI technology, and data-driven innovation to improve overall data governance, productivity, efficiency and enhance customer insights. Through its centralized structure, CADE focuses on enhancing the data platform, building consolidated source of truth datasets, dashboards and reports, conducting holistic deep dives and research, and driving AI innovation and data strategies.
Basic Qualifications
- 5+ years of SQL experience
- 5+ years of data engineering, database engineering, business intelligence or business analytics experience
- Experience in data warehouse technical architectures, data modeling, infrastructure components, ETL/ ELT and reporting/analytic tools and environments, data structures and hands-on SQL coding
- Knowledge of databases, storage and big data concepts
- Experience building/operating highly available, distributed systems of data extraction, ingestion, and processing of large data sets
- Bachelor's degree or above in a quantitative/technical field such as computer science, engineering, statistics
- Demonstrable advanced skills and experience using SQL with large data sets (e.g. Oracle, SQL Server, Redshift)
Preferred Qualifications
- 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)
- Experience working with both Batch and Real Time data processing systems
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, 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 https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, WA, Seattle - 132,100.00 - 178,800.00 USD annually