Senior Data Engineer - AWS ETL & DSP Data Pipelines

AgileEngine, LLC.; Medellín; Hybrid; COP 379,711,000 - 474,638,000; Full time

Benefits offered by this job

Professional growth

Competitive USD compensation

A selection of exciting projects

Flextime

Job summary

AgileEngine seeks a Senior Data Engineer to replace a third-party ingestion tool with a custom ETL pipeline in an AWS environment. You will document current logic, evaluate build-vs-buy options like Fivetran, and build a standalone pipeline within AWS.

You will work with data science and backend teams to ensure ingestion output aligns with downstream dashboards and attribution models, while maintaining English-language collaboration across globally distributed teams.

Qualifications

• 4+ years in large-scale data ingestion and SQL.
• Direct or adjacent experience with digital advertising / DSP data.
• Working knowledge of AWS data services within a cloud environment.
• Ability to contribute to application and backend work beyond ETL.
• Familiarity with evaluating ELT tools such as Fivetran.

Responsibilities

• Document current ingestion logic in the legacy tool and data sources.
• Build the ETL pipeline to replace the legacy system within AWS.
• Evaluate off-the-shelf vs. custom ETL solutions with the Infra Lead.
• Design a standalone, owned pipeline powering client dashboards.
• Coordinate with data science teams to align ingestion with scoring models.
• Work within the existing AWS environment (ECS, S3, RDS).

Skills

SQL

Data ingestion

DSP data

AWS services

ETL pipelines

Fivetran

English - upper-intermediate

Tools

Fivetran

AWS (ECS, S3, RDS)

Job description

AgileEngine seeks a Senior Data Engineer to replace a third-party ingestion tool with a custom ETL pipeline in an AWS environment. You will document current logic, evaluate build-vs-buy options like Fivetran, and build a standalone pipeline within AWS.

You will work with data science and backend teams to ensure ingestion output aligns with downstream dashboards and attribution models, while maintaining English-language collaboration across globally distributed teams.