Data Engineer- Building Next Gen Financial systems, FinAuto - Payables Tech

- Experience as a data engineer or related specialty (e.g., software engineer, business intelligence engineer, data scientist) with a track record of manipulating, processing, and extracting value from large datasets
- Knowledge of batch and streaming data architectures like Kafka, Kinesis, Flink, Storm, Beam. Knowledge of AWS tools like Airflow, GLUE, and EMR

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.
Interested to build the next generation Financial systems that can handle billions of dollars in transactions? Interested to build highly scalable next generation systems that could utilize Amazon Cloud?
Massive data volume, complex business rules in a highly distributed service oriented architecture, a world class information collection and delivery challenge. Our challenge is to deliver the software systems which accurately capture, process, and report on the huge volume of financial transactions that are generated each day as millions of customers make purchases, as thousands of Vendors and Partners are paid, as inventory moves in and out of warehouses, as commissions are calculated, and as taxes are collected in hundreds of jurisdictions worldwide.
The ideal candidate will draw upon exemplary analytical, critical thinking and problem solving skills, deep software development experience, and a passion for creating maintainable, highly reliable and scalable user facing applications that are accessed by thousands of external Vendors and internal Customers. Successful members of this team collaborate effectively with internal customers, other dependent development teams in Amazon to develop new applications successfully against high operational standards of system availability and reliability. In the space of workflow management tools, engineers in this team solve problems for the first time and have got opportunity to convert them to generic/re-usable components for use in broader engineering community. We look for engineers who are excellent communicators, self-motivated, flexible, hardworking, and who like to have fun. This candidate also plays active role in reviewing the technical designs from the team and in mentoring of other developers in the team.
You will have the opportunity to play a key role in building new software products and features from the ground up. Your work will allow you to utilize a melting pot of technologies, programming languages, and systems and require you to keep up with the ever-changing technological landscape. Your responsibilities will include all aspects of Data Engineering, with the freedom and encouragement to explore your own ideas and the reward of seeing your contributions benefit Amazon worldwide. Finance Automation team is looking for a talented software development engineer who can tackle large complex projects.

Key job responsibilities
Design, build and own components of a high volume data warehouse.
Build efficient data models using industry best practices and metadata for ad hoc and pre-built reporting.
Interface with business customers, gathering requirements and delivering complete data and reporting solutions owning the design, development, and maintenance of ongoing metrics, reports, analyses, and dashboards to drive key business decisions.
Continually improve ongoing reporting and analysis processes, automating and simplifying self-service support capabilities for our customers.
Interface with other technology teams to extract, transform, and load (ETL) data from a wide variety of data sources.
Own the functional and non-functional scaling of software systems in your area.
Provide input and recommendations on technical issues to other engineers, business stake holders, and data analysts.
Collaborate with data scientists to continue to build and enhance new or existing ML programs.