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Join Amazon's Selling Partner Insights and Analytics (SPIA) organization as a Data Engineer, where you'll build the data infrastructure powering 200+ business teams across Amazon Stores. Shape the future of data engineering for a platform processing millions of cases daily, driving insights that directly impact customer experience and operational excellence across the world's largest e-commerce ecosystem.
Paragon is a case management system (CMS) used by 180+ business teams within Stores to manage their communications and interactions with their customers. These business teams using Paragon are our enterprise customers. Paragon CMS includes: a customizable workbench UI, a lifecycle manager, a routing layer, such as tenant configuration, case storage, security, and data insights and analytic. The successful candidate is expected to contribute to all parts of the data engineering and deployment lifecycle, including design, development, documentation, testing and maintenance. They must possess good verbal and written communication skills, be self-driven and deliver high quality results in a fast paced environment. You will thrive in our collaborative environment, working alongside accomplished engineers who value teamwork and technical excellence. We're looking for experienced technical leaders.
Key job responsibilities
1. Design/implement automation and manage our massive data infrastructure to scale for the analytics needs of case management.
2. Build solutions to achieve BAA(Best At Amazon) standards for system efficiency, IMR efficiency, data availability, consistency & compliance.
3. Enable efficient data exploration, experimentation of large datasets on our data platform and implement data access control mechanisms for stand-alone datasets
4. Design and implement scalable and cost effective data infrastructure to enable Non-IN(Emerging Marketplaces and WW) use cases on our data platform
5. Interface with other technology teams to extract, transform, and load data from a wide variety of data sources using SQL, Amazon and AWS big data technologies
6. Must possess strong verbal and written communication skills, be self-driven, and deliver high quality results in a fast-paced environment.
7. Drive operational excellence strongly within the team and build automation and mechanisms to reduce operations
8. Enjoy working closely with your peers in a group of very smart and talented engineers.
- 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)
- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
- Experience with any ETL tool like, Informatica, ODI, SSIS, BODI, Datastage, etc.
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 for more information. If the country/region you're applying in isn't listed, please contact your Recruiting Partner.
Job ID: 148634959
Skills:
graph databases , object storage , Sql, Data Modeling, AWS Glue, IAM roles and permissions, non-relational databases, ETL pipelines, column-family databases, key-value stores
Skills:
Nosql, Gcp, Apis, Docker, Azure, Python, Sql, Kubernetes, AWS, time-series database systems
Skills:
snowflake , S3, Csv, Pyspark, Pl Sql, Sparksql, Json, Sql, Apache Airflow, Lambda, Aws Cloud, Abinitio ETL, Parquet, DataFrames, Iceberg, RDD, Data ingestion, Lake Formation, dbt, Glue, Snowflake Tasks, Athena, AWS Data Analytics Technology Stack
Skills:
Data Modeling, Data Warehousing, Sql, Git, Gcp, Spark, Databricks, Azure, Python, AWS, BI tools integration, ETL pipelines
Skills:
Unix, Data Quality, Data Modeling, Pyspark, AWS Glue, Data Warehousing, Data Governance, Database Design, Etl, Cloud Platforms, Pyspark SQL
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