Senior Data Platform Lead for AI/ML Readiness

Cloudbeds; Santiago; Remote; CLP 146,199,000 - 204,678,000; Full time

Benefits offered by this job

Remote-first culture

PTO in line with local law

Fully paid parental leave

Home office stipend

Job summary

Cloudbeds is seeking a senior data engineer/architect to guide architecture, platform governance, and MLops. You will lead data pipelines, optimize costs, and drive AI-readiness initiatives across a remote, globally distributed team.

You will collaborate with Engineering, Infrastructure, Data Science, and business stakeholders to ensure a reliable, scalable data ecosystem and mentor a team of Analytical Engineers in a fast-paced environment.

Qualifications

• Senior Hands-On Experience as a Data Engineer, Data Architect, or Tech Lead in cloud data environments.
• Proven track record building modern data architectures for analytics and AI readiness.
• Strong SQL and Python skills for data pipelines, ML workflows, and tooling integration.
• Experience with Snowflake, dbt, and data governance/observability practices.

Responsibilities

• Design and own cloud data architecture including Snowflake data models and ADRs.
• Lead end-to-end data pipelines, from ingestion to orchestration and deployment.
• Embed data discipline into Git/CI-CD workflows with automated tests and secure credentials.
• Define governance and observability strategies using data catalogs and lineage tracking.
• Mentor Analytical Engineers and guide architecture discussions in an Agile setup.

Skills

Data Architecture

Snowflake

dbt

Python

SQL

Data Pipelines

MLOps

CI/CD

Data Governance

API Integrations

Tools

Matillion

Sigma

Git

Airflow

Dagster

Fivetran

Job description

Cloudbeds is seeking a senior data engineer/architect to guide architecture, platform governance, and MLops. You will lead data pipelines, optimize costs, and drive AI-readiness initiatives across a remote, globally distributed team.

You will collaborate with Engineering, Infrastructure, Data Science, and business stakeholders to ensure a reliable, scalable data ecosystem and mentor a team of Analytical Engineers in a fast-paced environment.