Tech Lead Data Engineer in Vancouver

At Cloudbeds, we're not just building software, we're transforming hospitality. Our platform powers hotels across 150 countries, processing billions in bookings annually. From boutique properties to large hotel groups, we help hoteliers transform operations and uplevel their commercial strategy through a unified platform that integrates with hundreds of partners. And we do it with a completely remote team.

Imagine working alongside global innovators to build AI-powered solutions that solve hoteliers' biggest challenges. Since our founding in 2012, we've continually been recognized for excellence, most recently earning top honors at the 2026 HotelTechAwards for Best Hotel Management Software and landing on Deloitte's Technology Fast 500 again, but we're just getting started.

We are a small, senior, fully remote team running analytics for the entire company on Snowflake, dbt, Matillion, and Sigma. We move fast and ship things people use daily. What we're missing is a senior technical voice to make the foundational calls — architecture, platform, governance, ML Ops — so that speed compounds instead of accumulating debt. That's this role, and it comes with unusual latitude to decide how the platform should work.

You will help continuously modernize our tech stack, elevating our software engineering discipline across our data pipelines (DataOps/CI-CD), optimizing compute performance and costs, driving MLOps workflows, and reinforcing data quality and security standards. Crucially, you will drive our AI-readiness initiatives—architecting the semantic layers, data structures, and access patterns needed to make our data seamless and reliable for AI agents and automated workflows.

You will serve as the technical anchor for a talented team of Analytical Engineers, collaborating closely with Engineering, Infrastructure, Data Science / ML and business stakeholders to ensure our data ecosystem is reliable, scalable, and business-ready.

• Data Architecture & Modeling Ownership: Set and evolve our cloud data architecture on Snowflake. Own data model design across data products and marts, implementing flexible dimensional models (Star Schemas, SCDs, conformed dimensions) and documenting decisions through Architecture Decision Records (ADRs).
• Data Engineering Competency: Lead the end-to-end design and execution of data pipelines—owning ingestion, API integrations, custom connectors, ELT/ETL processes, and pipeline orchestration.
• DataOps & CI/CD Integration: Build upon our existing Git, CI/CD, and Jira workflows by embedding specialized data discipline—such as automated dbt testing on pull requests, environment isolation, automated credential management, and data deployment standards.
• Governance & Observability Strategy: Build upon and refine the initial foundations of our governance model. Evaluate, select, and implement scalable Data Catalog and Data Observability solutions to track data lineage, ensure freshness, monitor completeness, and automate failure alerting.
• Platform Efficiency & Cost Control: Continuously tune Snowflake query performance, clustering, and warehouse sizing to optimize execution times and compute spend.
• Technical Mentorship & Agile Leadership: Raise the technical effectiveness of Analytical Engineers within our Agile planning framework through architectural guidance, design reviews, and constructive challenge.
• Stack Modernization & Architecture Evolution: Continuously evaluate new tools, frameworks, and architecture patterns to modernize, optimize, and add flexibility to our data stack.
• Senior Hands-On Experience: Proven track record as a Senior Data Engineer, Data Architect, or Tech Lead in cloud data environments.
• AI & Agent Readiness: Exposure to preparing data structures, semantic models, feature stores, and API/access layers for AI agents, machine learning pipelines, and automated LLM workflows.
• Snowflake & dbt Mastery: Deep hands‑on expertise administering, tuning, and modeling in Snowflake, alongside advanced mastery of dbt (macros, incremental models, custom tests, packages, and dbt‑native CI/CD testing).
• Advanced SQL & Python Skills: Expert analytical SQL skills (query execution plan tuning, window functions, CTEs) and strong Python proficiency for data pipelines, scripting, and MLOps workflows.
• Orchestration & Tool Evaluation: Practical experience with ingestion and orchestration engines (e.g., Matillion, Apache Airflow, Prefect, Dagster, Fivetran), with a strong ability to evaluate architectural trade-offs, performance, and costs when selecting or migrating platforms.
• DataOps & Software Engineering Discipline: Experience operating with active Git and CI/CD pipelines, with a track record of extending these systems to include automated data testing, deployment validation, and secure credential rotation.
• Critical Thinking & Problem Decomposition: Tendency to question assumptions, gather full business context, form hypotheses, and rigorously evaluate outputs (especially for AI/ML outputs) before jumping to technical solutions.
• Pragmatic Experimentation: Passion for building lightweight MVPs and Proofs of Concept (POCs) to evaluate new technologies, test architectural hypotheses, and modernize the data stack.
• Communication & Technical Influence: Business-level English fluency with the ability to articulate complex technical trade-offs clearly, mentor teammates, and collaborate effectively in a remote, globally distributed team.

Cloudbeds is proud to be an Equal Opportunity Employer that celebrates the diversity in our global team! We do not discriminate based upon race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. Cloudbeds is committed to the full inclusion of all qualified individuals.