Remote Data Engineer for Cost Attribution & Financial Modeling

AgileEngine, LLC.

Santiago del Estero

A distancia

ARS 106.626.000 - 167.555.000

Jornada completa

Hace 3 días
Sé de los primeros/as/es en solicitar esta vacante
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Transforma esta oferta en una entrevista — un currículum y una carta de presentación creados pensando en lo que quiere el empleador.

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Ventajas ofrecidas por este puesto de trabajo

Professional growth
Competitive USD-based compensation
A selection of exciting projects
Flextime

Descripción de la vacante

AgileEngine is seeking a Data Engineer to build data pipelines and tooling for cost attribution, optimization, and financial modeling. You will enable product, engineering, analyst, and finance stakeholders to make faster, data-driven decisions and trust the data.

The role focuses on developing end-to-end ELT/ETL pipelines, data modeling, and scalable data infrastructure, with emphasis on cost transparency and performance-informed modeling.

Formación

  • 4+ years of experience in Data Engineering roles.
  • Hands-on experience with SQL, Python, Spark, and Scala.
  • Hands-on experience with workflow orchestration tools: dbt, Airflow, Dagster.
  • Hands-on experience with AWS Glue, Aurora, Athena, GCP, and Snowflake.
  • Experience architecting end-to-end ELT/ETL pipelines including data modeling.
  • Exposure to CI/CD development, IaC (Terraform), and REST API programming.

Responsabilidades

  • Develop tooling and datasets for cost attribution and financial modeling.
  • Build and maintain data pipelines providing cost transparency and optimization opportunities.

Conocimientos

SQL
Python
Spark
Scala

Herramientas

dbt
Airflow
Dagster
AWS
Glue
Aurora
Athena
GCP
Snowflake
Terraform
REST API

Descripción del empleo

AgileEngine is seeking a Data Engineer to build data pipelines and tooling for cost attribution, optimization, and financial modeling. You will enable product, engineering, analyst, and finance stakeholders to make faster, data-driven decisions and trust the data.

The role focuses on developing end-to-end ELT/ETL pipelines, data modeling, and scalable data infrastructure, with emphasis on cost transparency and performance-informed modeling.

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