Data Engineer Senior

Worldwide | Oct. 7, 2026

Report as Closed

Company: DSAC Chile

Country: Worldwide

Type: Remote

Employment: Full-time

Description: DSAC is a Chilean technology company specialized in the development of technological solutions, artificial intelligence, cybersecurity and automation for companies seeking to improve their efficiency and generate real impact on their business. None

Training in Civil Computer Engineering, Industrial Engineering, Computing or related career.
4+ years of experience as a Data Engineer.
At least 2 years of experience working with GCP and BigQuery.
Experience building productive pipelines with Airflow / Cloud Composer.
Experience working with large volumes of data.
Proficiency in Git and flows. CI/CD.
Experience with Python and advanced SQL.
Ability to work autonomously and make technical decisions.
Rigor and orientation to the quality and completeness of data.
Ability to document and generate knowledge.
Clear communication with technical and business teams.
Criteria to optimize costs and performance in the cloud.

Main Objective:

Design, develop, implement and maintain data engineering solutions that allow the integration, processing, storage and availability of reliable, secure and quality information to support the operational, analytical and business needs of the Company and/or its clients.

What will be your challenge?

You will be responsible for designing and building reliable, automated and scalable data ingestion and processing solutions, ensuring the quality, completeness and availability of information.

Your main responsibilities will include:

Design and build productive data pipelines.
Implement ingestion and processing processes using Google Cloud Platform (GCP) services.
Work with BigQuery and Cloud Storage for data storage and processing.
Design efficient tables considering partitioning, clustering, late data and time zones.
Orchestrate and monitor pipelines using Cloud Composer / Airflow.
Implement completeness controls, volumetry and alerts for anomalous behavior.
Work with large volumes of information and optimize processes considering performance and costs in the cloud.
Document pipelines, operating procedures, runbooks and reprocessing/backfill processes.
Work collaboratively with Data, AI and Data Science teams.
Participate in the definition and evolution of solutions within an existing data platform.