Remote Senior Data Platform Lead: Snowflake & MLOps

Third-Party Job Posts; United States; Remote; CLP 117,763,000 - 176,644,000; Full time

Benefits offered by this job

Remote-first

Remote work

Home office stipend

Paid time off

Professional development

Job summary

Cloudbeds is seeking a senior data engineer/architect to lead data platform modernization, governance, and ML readiness. You will own data modeling, pipelines, and tooling choices for a globally distributed, remote team. Strong cloud experience and leadership are essential.

You will collaborate with engineering, data science, and business stakeholders to ensure reliable, scalable data assets and AI-ready workflows while optimizing compute and costs.

Qualifications

• Senior data engineering/architectural background in cloud data environments.
• Proven ability to guide data platforms, governance, and ML readiness.
• Strong SQL, Python, and data modeling skills with practical CI/CD experience.

Responsibilities

• Own data architecture and modeling across data products and marts.
• Lead end-to-end data pipelines including ingestion, APIs, ETL/ELT.
• Advance DataOps, CI/CD, automated testing, and environment isolation.
• Shape governance, data catalog, and observability strategies.
• Tune Snowflake performance and optimize costs.
• Provide technical mentorship to Analytical Engineers in an Agile setup.
• Evaluate new tools and patterns to modernize the data stack.
• Contribute to AI/LLM workflows and semantic layer design.

Skills

Advanced SQL

Python programming

Cloud data architecture

Data governance

Mentorship & leadership

CI/CD / DataOps

AI/ML data readiness

English communication

Tools

Snowflake

dbt

Sigma

Matillion

Apache Airflow

Prefect

Dagster

Fivetran

Job description

Cloudbeds is seeking a senior data engineer/architect to lead data platform modernization, governance, and ML readiness. You will own data modeling, pipelines, and tooling choices for a globally distributed, remote team. Strong cloud experience and leadership are essential.

You will collaborate with engineering, data science, and business stakeholders to ensure reliable, scalable data assets and AI-ready workflows while optimizing compute and costs.