At Amazon, we are committed to being the most customer-centric company on earth. The North American Transportation Services (NATS) team at Amazon is comprised of high-powered dynamic teams that are shaping network planning and execution through the development and application of innovative transportation and delivery management concepts.
NATS Transportation Planning team is looking for a customer-focused, detail-oriented, experienced Data Engineer II to help us build an enterprise grade data lake with the latest AWS data services. This role is inherently cross-functional—this leader will work closely with business intelligence engineers, data scientists, software engineers, program management, design, operations, finance, legal, business development, customer service, and executive teams to support analytical framework with efficient data pipelines. As a Data Engineer, you will be responsible for architecture and engineering of data infrastructure across our systems of transportation planning, development, production and test. You will provide analytical support by managing data store, develop and track key performance indicators and help make data driven program decisions. This role will be designing and developing ingestion of data that will provide insights into our business health.
Key job responsibilities
- Design, implement and maintain data infrastructure including data modeling, ETL pipelines, and ongoing maintenance.
- Partner with product, operational, and technical teams to build data pipelines from a wide variety of sources using AWS big data technologies (Lake Formation, Glue, S3, MWAA, Lambda, etc.).
- Able to read, write, and debug data processing and orchestration code written in Python/Scala etc following best coding standards (e.g. version controlled, code reviewed, etc.)
- Build a data dictionary, catalog and governance plan and manage and audit all of the registered data via mechanisms.
- Work with data consumers to provide and correlate the right data for business intelligence and machine learning use-cases.
- Develop automated solutions to minimize manual processes with focus on efficiency and scalability.
- Provide guidance on data stewardship for teams onboarding to the central data systems.
