Data Engineer, Conversational Shopping Data Engineering
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As a Data Engineer, you will be working on building and maintaining complex data pipelines, assemble large and complex datasets to generate business insights and to enable data driven decision making and support the rapidly growing and dynamic business demand for data.
Data Engineers help us design, manage, and continuously enhance our Analytics needs. You will develop and deliver analytics applications including metrics generation, metrics correlation, modeling, and many other use cases to help improve process effectiveness, customer experience, and automation. As a DE, you will deal with technical aspects of data warehousing (Redshift, Spectrum, EMR, ETL), infrastructure and build data pipelines, tools, and reports that enable program managers, analysts, BIE's, solution architects, and executives to design and deliver bench marking services for Amazon's business units. We want to you to feel welcomed, included and valued right from the start. We know that your experiences will help us build a better world
Key job responsibilities
. Design data schema and operate internal data warehouses and SQL/NOSQL database systems.
. Design data models, implement, automate, optimization and monitor data pipelines
. Own the design, development and maintenance of ongoing metrics, reports, analyses, dashboards, etc. to drive key business decisions
. Analyze and solve problems at their root, stepping back to understand the broader context
. Manage Redshift/Spectrum/EMR infrastructure, and drive architectural plans and implementation for future data storage, reporting, and analytic solutions
. Work on different AWS technologies such as S3, Redshift, Lambda, Glue, etc.. and Explore and learn the latest AWS technologies to provide new capabilities and increase efficiency
. Work on data lake platform and different components in the data lake such as Hadoop, Amazon S3 etc.
. Work on SQL technologies on Hadoop such as Spark. Hive, Impala etc.
. Recognize and adopt best practices in reporting and analysis: data integrity, test design, analysis, validation, and documentation.
. Must possess verbal and written communication skills, be self-driven, and deliver high quality results in a fast-paced environment.
. Conduct rapid prototyping and proof of concepts
. Conceptualize and develop automation tools for bench marking data collection and analytics
. Interface with other technology teams to extract, transform, and load data from a wide variety of data sources using SQL and AWS big data technologies
A day in the life
0
- 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: 152286313
Skills:
amazon emr , amazon dynamodb , Aws Lambda, Amazon Redshift, Amazon S3, Amazon Kinesis, Apache Spark, Amazon Rds, Etl
Skills:
Pyspark, Power Bi, Azure Synapse Analytics, Azure Databricks, Power Query, Dax, Python, Sql, Azure Data Engineer
Skills:
Gcp, Powerbi, Tableau, Azure, Python, Sql, AWS, ETL ELT frameworks tools
Skills:
snowflake , Upgrades, Configuration, Tableau, Information Design Tool, Central Management Console, Python, AWS, Automation Scripting, Power Bi, PowerShell, Shell Scripting, Windows Server Administration, Sql, Migrations, Qlik, Databricks, Azure, ITIL-based support frameworks, Patching, database troubleshooting, Linux Unix operating systems, DevOps practices, SAP BO BI 4.x platform administration, SAP BusinessObjects Administration, CI CD pipelines, File Repository Server, SAP BO installation, release automation
Skills:
Schema Design, Data Modeling, Data Warehousing Concepts, Snowflake Data Warehouse, Integration techniques, Data pipelines, Cloud-based data platforms, ETL workflows
