Senior Data Architect & Tech Lead — Remote

Cloudbeds; Caracas; Remote; VES 105,171,000 - 131,464,000; Full time

Benefits offered by this job

Remote First

PTO per local law

Home office stipend

Cloudbeds University

Professional development

Job summary

Cloudbeds is seeking a Senior Data Engineer / Tech Lead to shape our Data Analytics platform. You will own data architecture, modeling, and governance for Snowflake and data products. Lead end-to-end data pipelines, drive DataOps/CI-CD, and mentor a distributed team of Analytical Engineers in a remote setting.

You will advance ML readiness, optimize costs, and ensure high data quality across staging to marts while collaborating with Engineering, Data Science, and business stakeholders.

Qualifications

• Senior data engineering background with cloud data environments
• Experience architecting data platforms and governance
• Hands-on leadership in data pipelines, ML workflows
• Strong SQL and Python for data pipelines
• Familiarity with dbt, Snowflake, and modern ELT/ETL tools

Responsibilities

• Own data architecture and modeling for Snowflake and data products
• Lead end-to-end data pipeline design and execution
• Embed data discipline in CI/CD with automated testing
• Develop governance, observability, and data catalog strategies
• Tune query performance and manage compute costs in Snowflake
• Mentor teammates and guide agile data teams
• Evaluate and modernize data stack architectures

Skills

Senior Data Engineer

Data Architect

Tech Lead

AI & ML Ops

Data Modeling

SQL proficiency

Python

Data Governance

Cloud Snowflake

Tools

Snowflake

dbt

Matillion

Sigma

Airflow

Dagster

Prefect

Fivetran

Job description

Cloudbeds is seeking a Senior Data Engineer / Tech Lead to shape our Data Analytics platform. You will own data architecture, modeling, and governance for Snowflake and data products. Lead end-to-end data pipelines, drive DataOps/CI-CD, and mentor a distributed team of Analytical Engineers in a remote setting.

You will advance ML readiness, optimize costs, and ensure high data quality across staging to marts while collaborating with Engineering, Data Science, and business stakeholders.