Amazon Data Engineer I, Payment Acceptance &Experience in Seattle, Washington
The Payment Acceptance & Experience (PAE) team is looking for a Data Engineer with a deep understanding of the full life-cycle of data generation and application. The PAE team is responsible for how Amazon’s customers pay on Amazon’s sites and through Amazon’s services around the globe.
About the Role
As a Data Engineer I, you will design, develop, implement, test, and operate large-scale, high-volume, high-performance data structures for analytics, reporting and machine learning. You will leverage your expertise in data pipelines and persistence to build systems and tools that lower the cost of performing advanced analysis while also expanding the number and types of analyses that can be performed. You will take the lead in identifying architecture deficiencies, and solving for the same by building cutting edge software solutions.
Key job responsibilities
• Implement data ingestion routines, both real time and batch using best practices in data modeling
• Develop ETL/ELT processes leveraging AWS technologies and Big data tools.
• Gather business and functional requirements and translate these requirements into robust, scalable, operable solutions that work well within the overall data architecture.
• Analyze source data systems and drive best practices in source teams.
• Participate in the full development life cycle, from design, implementation and testing, to documentation, delivery, support, and maintenance.
• Produce comprehensive, usable dataset documentation and metadata.
• Evaluate and make recommendations around dataset implementations designed and proposed by peer data engineers.
• Evaluate and make recommendations around the use of new or existing software products and tools.
We are open to hiring candidates to work out of one of the following locations:
Seattle, WA, USA
1+ years of data engineering experience
Experience with data modeling, warehousing and building ETL pipelines
Experience with one or more query language (e.g., SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, Scala)
Experience with one or more scripting language (e.g., Python, KornShell)
Knowledge of writing and optimizing SQL queries in a business environment with large-scale, complex datasets
Knowledge of AWS Infrastructure
Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
Experience with any ETL tool like, Informatica, ODI, SSIS, BODI, Datastage, etc.
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 $81,000/year in our lowest geographic market up to $185,000/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. Applicants should apply via our internal or external career site.
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