Tech Lead Data Engineer

New

CloudbedsHospitality software

Location: Chile; Colombia; Ecuador; Honduras; Latin America; Mexico; PeruFull-TimeLead

Salary not disclosed

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Job Details

Proven track record as a Senior Data Engineer, Data Architect, or Tech Lead in cloud data environments.
Deep hands-on expertise administering, tuning, and modeling in Snowflake.
Advanced dbt mastery, including macros, incremental models, custom tests, packages, and dbt-native CI/CD testing.
Expert analytical SQL skills, including query execution plan tuning, window functions, and CTEs.
Strong Python proficiency for data pipelines, scripting, and MLOps workflows.
Practical experience with ingestion and orchestration engines such as Matillion, Apache Airflow, Prefect, Dagster, or Fivetran.
Experience operating with Git and CI/CD pipelines and extending them with automated data testing, deployment validation, and secure credential rotation.
Exposure to preparing data structures, semantic models, feature stores, and API/access layers for AI agents, machine learning pipelines, and automated LLM workflows.
Ability to evaluate architectural trade-offs, performance, and costs when selecting or migrating platforms.
Business-level English fluency and ability to articulate technical trade-offs, mentor teammates, and collaborate remotely.
Experience or interest in building lightweight MVPs and proofs of concept to evaluate technologies and architectural hypotheses.

Set and evolve the cloud data architecture on Snowflake.
Own data model design across data products and marts, including dimensional models, Star Schemas, SCDs, and conformed dimensions.
Document architecture decisions through Architecture Decision Records (ADRs).
Lead the end-to-end design and execution of data pipelines, including ingestion, API integrations, custom connectors, ELT/ETL, and orchestration.
Extend Git, CI/CD, and Jira workflows with automated dbt testing, environment isolation, credential management, and data deployment standards.
Evaluate, select, and implement Data Catalog and Data Observability solutions for lineage, freshness, completeness, and failure alerting.
Tune Snowflake query performance, clustering, and warehouse sizing to optimize execution times and compute spend.
Guide Analytical Engineers through architectural guidance, design reviews, and constructive challenge.
Evaluate tools, frameworks, and architecture patterns to modernize the data stack.
Architect semantic layers, data structures, and access patterns for AI agents and automated workflows.

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