Tech Lead Data Engineer

New

CloudbedsHospitality software

Location: Canada; Latin AmericaFull-TimeLead

Salary not disclosed

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Job Details

Bring a proven track record as a Senior Data Engineer, Data Architect, or Tech Lead in cloud data environments.
Have deep hands-on expertise administering, tuning, and modeling in Snowflake.
Have advanced dbt expertise, including macros, incremental models, custom tests, packages, and dbt-native CI/CD testing.
Demonstrate expert analytical SQL skills, including query execution plan tuning, window functions, and CTEs.
Have strong Python proficiency for data pipelines, scripting, and MLOps workflows.
Have practical experience with data ingestion and orchestration engines such as Matillion, Apache Airflow, Prefect, Dagster, or Fivetran.
Have experience with Git and CI/CD pipelines, including automated data testing, deployment validation, and secure credential rotation.
Bring exposure to preparing data structures, semantic models, feature stores, and API/access layers for AI agents, machine learning pipelines, and automated LLM workflows.
Demonstrate the ability to evaluate architectural trade-offs, performance, and costs when selecting or migrating platforms.
Use critical thinking to gather business context, form hypotheses, and evaluate technical and AI/ML outputs.
Be able to articulate technical trade-offs, mentor teammates, and collaborate effectively in a remote team.
Have business-level English fluency.

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.
Lead the 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 and implement data catalog and observability solutions for lineage, freshness, completeness, and failure alerting.
Tune Snowflake query performance, clustering, and warehouse sizing to improve execution times and control compute spend.
Guide and mentor Analytical Engineers through architectural guidance, design reviews, and Agile planning.
Drive AI-readiness by architecting semantic layers, data structures, and access patterns for AI agents and automated workflows.
Evaluate tools, frameworks, and architecture patterns to modernize and optimize the data stack.

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