AWS Data Engineering Lead - Cost Attribution & ELT | Remote

Perficient; Ciudad de Mendoza; Hybrid; ARS 92,241,000 - 184,482,000; Full time

Benefits offered by this job

Professional growth

USD-based compensation

Exciting projects

Flextime and hybrid

Job summary

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

You will work with SQL, Python, Spark, Scala, and modern orchestration tools to implement end-to-end ELT/ETL pipelines, while emphasizing data quality and measurable impact on cost efficiency.

Qualifications

• 4+ years of data engineering experience.
• Proficient in SQL, Python, Spark, and Scala.
• Experience with dbt, Airflow, and Dagster.
• Experience with AWS, GCP, and Snowflake.
• End-to-end ELT/ETL pipelines design and maintenance.
• CI/CD and Infrastructure as Code (Terraform).
• Strong data auditing and validation skills.
• Ability to explain technical concepts to non-technical audiences.
• Upper-intermediate English.

Responsibilities

• Develop tooling and datasets for telemetry-driven cost attribution and financial modeling.
• Build and maintain data pipelines to provide cost transparency and surface optimization opportunities.

Skills

SQL

Python

Spark

Scala

dbt

Airflow

Dagster

AWS

GCP

Snowflake

ELT/ETL pipelines

Terraform

REST API

CI/CD

Data auditing

English (upper-intermediate)

Tools

Datadog API

Job description

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

You will work with SQL, Python, Spark, Scala, and modern orchestration tools to implement end-to-end ELT/ETL pipelines, while emphasizing data quality and measurable impact on cost efficiency.