Bilingual Lead Data Engineer: GCP & BigQuery Expert

Anonymous; Bogotá; Hybrid; COP 100,000,000 - 200,000,000; Full time

Benefits offered by this job

On-site with some remote days

9:00–18:00 schedule

Job summary

Anonymous in Bogota is seeking a Bilingual Lead Data Engineer to drive architecture and reliability of our data platform on GCP. You will lead data pipelines, optimize BigQuery models, and mentor engineers to ensure high quality and scalable solutions.

The role emphasizes hands-on leadership, collaboration with Analytics and Data Science, and ongoing cost‑efficiency improvements, with a work model that is primarily on-site and occasional remote days.

Qualifications

• Significant experience in Data Engineering with leadership exposure.
• Expertise in GCP and BigQuery for scalable analytics.
• Hands-on with Cloud Run, Dataflow, Pub/Sub, Airflow/Composer.
• IaC experience, preferably Terraform.
• Strong data modeling, orchestration and prod know‑how.
• Proven leadership, mentoring and problem solving.
• Fluent in English and Spanish.

Responsibilities

• Lead data architecture and engineering decisions across our GCP data platform.
• Design, build, and optimize scalable data pipelines and ELT workflows.
• Develop and optimize BigQuery data models to power analytics and BI.
• Establish best practices for code quality, testing, monitoring, security, and governance.
• Mentor engineers and collaborate with Analytics, Data Science, and cross-functional teams.
• Improve platform reliability, performance, and cost efficiency.

Skills

Data engineering

Technical leadership

Mentoring

Bilingual English/Spanish

Tools

Terraform

Cloud Run

Dataflow

Pub/Sub

Airflow / Cloud Composer

BigQuery

Job description

Anonymous in Bogota is seeking a Bilingual Lead Data Engineer to drive architecture and reliability of our data platform on GCP. You will lead data pipelines, optimize BigQuery models, and mentor engineers to ensure high quality and scalable solutions.

The role emphasizes hands-on leadership, collaboration with Analytics and Data Science, and ongoing cost‑efficiency improvements, with a work model that is primarily on-site and occasional remote days.