Data Engineer I, EU AMXL BIT

Amazon Extra Large (AMXL) is looking for a customer focused, analytically and technically skilled Data Engineer to drive effective performance measurement and management, innovation and execution.

As a Data engineer on this team, you will have the exciting opportunity to contribute to one of the few E2E Organizations within Amazon. You will collaborate closely with program managers, engineers, analysts, and business teams to design and implement solutions tailored to their needs, requiring creativity and strong problem-solving skills. You will play a key role in developing data engineering roadmaps aimed at building platforms to address complex challenges in efficient and scalable ways. In this role, you will leverage your analytical skills to work backwards from customer problems and design solutions that meet their objectives.

Additionally, you will be responsible for designing, developing, and operating a data service platform using Python, Airflow, and SQL to build the various ETL, analytics, and data quality components. You’ll automate deployments using AWS CodeDeploy, AWS CodePipeline, AWS Cloud Development Kit (CDK), and AWS Cloud Formation. You will design and implement complex data models and build the end-to-end infrastructure for reports and dashboards to be created by our customers. You will work with AWS services like Redshift, Glue, S3, IAM, CloudWatch, and more.

Key job responsibilities Work with external data partners to establish AMXL connections to ingest various datasets Interface with other technology teams to extract, transform, and load data from a wide variety of data sources using different AWS products such as Lambda, Glue, EMR, Kinesis Firehose Maintain and enhance existing data pipelines Create extensible designs and easy to maintain solutions with the long term vision in mind Interface with cross functional teams, gathering requirements and delivering data solutions Improve tools, processes, scale existing solutions, create new solutions as required based on team and stakeholder needs

• Experience in data engineering
• 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)