Senior Remote Data Engineer — Cost Analytics

Bairesdev; Rosario; Hybrid; ARS 184,482,000 - 276,723,000; Full time

Benefits offered by this job

Professional growth

Competitive USD pay

Exciting projects

Flextime

Job summary

AgileEngine is seeking a Data Engineer to build data pipelines and tooling for cost attribution, optimization, and financial modeling. You will work with product, engineering, analyst, and finance teams to ensure data-driven decision making and trusted data.

You will design end-to-end ELT/ETL pipelines, implement data models, and contribute to CI/CD and IaC practices while embracing a strong, data-auditing mindset across projects.

Qualifications

• 4+ years of experience 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 end-to-end ELT/ETL data pipelines, data modeling, and schema maturity.
• Exposure to CI/CD development processes, Infrastructure as Code (Terraform), and REST API programming.
• Strong attention to detail, analytical skills, and data auditing and validation.
• Ability to explain technical concepts to non-technical audiences.

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

4+ years data eng

SQL

Python

Spark

Scala

dbt

Airflow

Dagster

AWS

GCP

Snowflake

ELT/ETL

CI/CD

Terraform

REST API

Data auditing

English

Tools

dbt

Airflow

Dagster

Terraform

Snowflake

AWS

GCP

Job description

AgileEngine is seeking a Data Engineer to build data pipelines and tooling for cost attribution, optimization, and financial modeling. You will work with product, engineering, analyst, and finance teams to ensure data-driven decision making and trusted data.

You will design end-to-end ELT/ETL pipelines, implement data models, and contribute to CI/CD and IaC practices while embracing a strong, data-auditing mindset across projects.