Senior Data Engineer: Cost Attribution & Financial Modeling

AgileEngine, LLC.; Bogotá; Hybrid; COP 287,292,000 - 383,056,000; Full time

Benefits offered by this job

Professional growth

Competitive USD-based compensation

Challenging projects with Fortune 500/

Flextime – hybrid/work-from-home

Job summary

AgileEngine, LLC. is seeking a Data Engineer to build data pipelines and tooling for cost attribution, optimization, and financial modeling.

You will develop datasets and tooling for telemetry-driven cost attribution, enabling stakeholders to make data-driven decisions and trust the data. The role emphasizes end-to-end ELT/ETL, data modeling, and CI/CD practices in a hybrid work setup, with a focus on data quality and governance.

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 and enhancing end-to-end ELT/ETL pipelines, data modeling, and schema maturity.
• Exposure to CI/CD development, Infrastructure as Code (Terraform), and REST API programming.
• Strong attention to detail, analytical skills, and data auditing.

Responsibilities

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

Skills

4+ years of experience in Data Eng

SQL

Python

Spark

Scala

CI/CD

REST API

data auditing

English (Upper-intermediate)

Education

Bachelor's degree in Computer Science or related

Tools

dbt

Airflow

Dagster

AWS Glue

AWS Aurora

AWS Athena

Snowflake

Job description

AgileEngine, LLC. is seeking a Data Engineer to build data pipelines and tooling for cost attribution, optimization, and financial modeling.

You will develop datasets and tooling for telemetry-driven cost attribution, enabling stakeholders to make data-driven decisions and trust the data. The role emphasizes end-to-end ELT/ETL, data modeling, and CI/CD practices in a hybrid work setup, with a focus on data quality and governance.