Data Engineer II, XFN (Cross-Border Fulfilment Network)

at  Amazon EU SARL

• Experience in data engineering
• Experience with data modeling, warehousing and building ETL pipelines
  The EU INTech XFN team’s mission is to serve customers and drive the flywheel by reducing sourcing costs, increasing selection, and increasing availability by removing country borders as a barrier between supply and demand. Formed out of the convergence of the European Fulfillment Network (EFN) and the North America Fulfillment Network (NAFN) in Q4’21, XFN today supports Intra-Regional Retail inventory sharing across 14 stores and 3 networks in the EU, NA, and FE. We own services which determine and drive up cross-border eligibility, reduce expensive cross-border dropship fulfillment, and optimize upstream and downstream defects related to cross-border eligibility.
  Key job responsibilities
  The ideal candidate relishes working with large volumes of data, enjoys the challenge of highly complex technical contexts, and, above all else, is passionate about data and analytics. An expert with data modeling, ETL design and business intelligence tools and passionately partners with the business to identify strategic opportunities where improvements in data infrastructure creates out-sized business impact. The ideal candidate is self-starter, comfortable with ambiguity, able to think big (while paying careful attention to detail), and enjoys working in a fast-paced and global team. They should also have expertise in data engineering, SQL experience, as well as experience with data modeling, warehousing, and building ETL pipelines and proficiency with at least one of the contemporary scripting or programming languages, including NodeJS, Java, Scala, or Python.
  We’re also looking for someone with preferred experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions. Experience working independently on and completing end-to-end projects. Knowledge of professional software engineering and best practices for the full software development life cycle, including coding standards, software architectures, code reviews, source control management, continuous deployments, testing, and operational excellence.
  A day in the life
  At Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and we host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.
  Work/Life Balance
  Our team also puts a high value on work-life balance. Striking a healthy balance between your personal and professional life is crucial to your happiness and success here, which is why we aren’t focused on how many hours you spend at work or online. Instead, we’re happy to offer a flexible schedule so you can have a more productive and well balanced life—both in and outside of work.

A day in the life

• Create, design, build, test, document, and manage massively parallel, high-throughput, high-performance data structures for AMZL analytics and business intelligence.
• Use best practices for data modeling, ETL/ELT procedures, SQL, Redshift, and OLAP technologies to implement data structures.
• Collect and convert functional and business requirements into solutions that are operable, scalable, and well-suited to the overall data
• Determine best practices for creating data lineage from a range of data sources by analyzing source data systems. data sources.
• Engage in all phases of the development life cycle, including design, implementation, testing, delivery, documentation, support, and maintenance.
• Generate complete, reusable metadata and dataset documentation.
• Assess and decide on the implementation of datasets created and suggested by other data engineers.
• 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)
• Knowledge of batch and streaming data architectures like Kafka, Kinesis, Flink, Storm, Beam

Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice (https://www.amazon.jobs/en/privacy_page) to know more about how we collect, use and transfer the personal data of our candidates

Please refer the Job description for details