Geospatial Data Engineer for Global Sustainability

Amazon Inc.

Seattle (WA)

In loco

USD 132.000 - 179.000

Tempo pieno

14 giorni+

Una candidatura completa in un minuto

Curriculum e lettera di presentazione personalizzati, pronti da inviare.

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Descrizione del lavoro

Amazon is seeking a Data Engineer to extend geospatial capabilities and build scalable data infrastructure for sustainability metrics. The role focuses on area-based analysis, risk factor data, and interfaces with internal and external systems.

You will own data ingestion, orchestration, and the development of pipelines using Python/Scala/Java, AWS tools like Redshift and Glue, and contemporary data frameworks such as Spark and Airflow.

Competenze

  • 3+ years of data engineering experience.
  • Experience with data modeling, warehousing and ETL pipelines.
  • Experience delivering end-to-end projects independently.
  • Experience building/operating highly available, distributed data systems.
  • Deep experience with geospatial data types and GIS tools.

Mansioni

  • Own geospatial data operations, ingestion, documentation and metadata.
  • Design and implement methods to consolidate unstructured data.
  • Automate deployment of data pipelines using CI/CD and multiple languages.
  • Build data infrastructure using AWS services (Redshift, S3, Glue, EMR, Step Functions, EventBridge).
  • Develop and maintain ETL/ELT processes with PySpark, Spark, AWS Glue, Airflow, and SQL.

Conoscenze

Data engineering
Geospatial data
ETL pipelines
Python/Scala/Java
Distributed systems
Data modeling

Strumenti

Redshift
S3
Glue
EMR
Kinesis
FireHose
Lambda
IAM

Descrizione del lavoro

Amazon is seeking a Data Engineer to extend geospatial capabilities and build scalable data infrastructure for sustainability metrics. The role focuses on area-based analysis, risk factor data, and interfaces with internal and external systems.

You will own data ingestion, orchestration, and the development of pipelines using Python/Scala/Java, AWS tools like Redshift and Glue, and contemporary data frameworks such as Spark and Airflow.

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