Data Engineer: Cost Attribution & ELT Pipelines — Flextime

Sophilabs, Inc.; Ciudad de Mendoza; Híbrido; ARS 184.482.000 - 261.349.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Professional growth

Competitive USD-based compensation

A selection of exciting projects

Flextime: work from home and office

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 data-driven decisions and trust the data.

Responsibilities include developing datasets for telemetry-driven cost attribution, surface optimization opportunities, and maintaining end-to-end ELT/ETL pipelines with data modeling and schema maturity.

Formación

• 4+ years in Data Engineering roles.
• Hands‑on experience with SQL, Python, Spark, and Scala.
• Hands‑on experience with workflow orchestration tools such as dbt, Airflow, and Dagster.
• Hands‑on experience with AWS (Glue, Aurora, Athena), GCP, and Snowflake.
• Experience architecting and enhancing ELT/ETL data pipelines, including data modeling.
• Exposure to CI/CD development processes, Infrastructure as Code (Terraform), and REST API programming.
• Strong attention to detail, analytical skills, and data auditing.

Responsabilidades

• Develop and implement tooling and datasets for cost attribution and financial modeling.
• Build and maintain data pipelines that provide cost transparency and surface optimization opportunities.

Conocimientos

SQL

Python

Spark

Scala

REST API programming

Herramientas

dbt

Airflow

Dagster

AWS

GCP

Snowflake

Terraform

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 data-driven decisions and trust the data.

Responsibilities include developing datasets for telemetry-driven cost attribution, surface optimization opportunities, and maintaining end-to-end ELT/ETL pipelines with data modeling and schema maturity.