Lead Data Engineer - Remote & Flexible Hours

AgileEngine; Rosario; Presencial; ARS 178.643.000 - 267.965.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Growth without limits

Competitive compensation

Flexibility: 100% remote

Meaningful, modern projects

Collaborative culture

Well-being & support

Descripción de la vacante

AgileEngine is seeking a Lead Data Engineer to own the data pipeline and analytical architecture for a large-volume marketing analytics platform. You will define DAG-based orchestration, drive the AWS data stack, and lead senior developers while enforcing quality standards.

The role emphasizes autonomy, shaping solutions before implementation, with a focus on OLAP workloads, S3-backed data lakes, and real-time processing considerations. Remote, international collaboration is expected.

Formación

• 7+ years of engineering experience with ETL and large-volume data systems.
• Hands-on OLAP-style architecture using cloud data stacks.
• Strong design and governance of data pipelines and schemas.
• Experience with Airflow or similar orchestrators is required.

Responsabilidades

• Design and own ETL pipelines at scale from internal sources and APIs.
• Make architectural calls on partitioning, file formats, and near-real-time processing.
• Lead DAG-based workflows and define orchestration strategy with Airflow.
• Drive AWS data stack usage with DevOps to meet requirements.
• Review PRs and enforce code quality; guide senior developers.

Conocimientos

ETL design

OLAP architecture

Airflow

Python

REST/GraphQL

AWS data stack

Docker

PostgreSQL

Unix shell

AI tooling

Ambiguity handling

Herramientas

Dagster

Prefect

Luigi

Step Functions

Descripción del empleo

AgileEngine is seeking a Lead Data Engineer to own the data pipeline and analytical architecture for a large-volume marketing analytics platform. You will define DAG-based orchestration, drive the AWS data stack, and lead senior developers while enforcing quality standards.

The role emphasizes autonomy, shaping solutions before implementation, with a focus on OLAP workloads, S3-backed data lakes, and real-time processing considerations. Remote, international collaboration is expected.