[All remote jobs](https://jobicy.com/jobs.md)[![Cloudbeds logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2025/06/3aef1a38-221.png)](https://jobicy.com/company/cloudbeds.md)[Cloudbeds](https://jobicy.com/company/cloudbeds.md) # Tech Lead Data Engineer Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/cloudbeds.md)ShareRemote from[Canada](https://jobicy.com/job-region/canada.md), [LATAM](https://jobicy.com/job-region/latam.md)SalaryUndisclosedDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceSeniorPublished22 Oct 2026Apply before21 Nov 2026Listing views50Application actions3Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. Cloudbeds is seeking a senior hands-on data engineering leader to set the architecture and technical standards for its company-wide analytics platform. The role owns Snowflake warehouse design, dimensional modeling, dbt development practices, data pipelines, governance, observability, and cost optimization. It also leads AI-readiness work by designing reliable semantic layers, data structures, and access patterns for ML and agentic workflows. The Tech Lead mentors analytical engineers and partners with engineering, infrastructure, data science, and business stakeholders in a globally distributed remote organization. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 4/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 5/5IndependentCollaborative AI insightThis is a senior technical leadership position spanning architecture, platform operations, governance, DataOps, MLOps, and team mentorship. Success requires deep practical expertise while making high-impact technical trade-offs with considerable autonomy. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Market rate$165,000US market range (per year)$140k–$200k0$220.00k AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer salary was disclosed. The annual USD figures are AI-estimated market compensation for a senior remote Data Engineering Tech Lead / Data Architect role serving the US market; yearly cadence is inferred from the senior full-time professional role. ## Core skills Skills and capabilities most closely associated with this opportunity. [Snowflake](https://jobicy.com/jobs?search_keywords=Snowflake.md)[dbt](https://jobicy.com/jobs?search_keywords=dbt.md)[Data Architecture](https://jobicy.com/jobs?search_keywords=Data%20Architecture.md)[Dimensional Modeling](https://jobicy.com/jobs?search_keywords=Dimensional%20Modeling.md)[SQL](https://jobicy.com/jobs?search_keywords=SQL.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[ETL/ELT Pipelines](https://jobicy.com/jobs?search_keywords=ETLELT%20Pipelines.md)[DataOps](https://jobicy.com/jobs?search_keywords=DataOps.md)[CI/CD](https://jobicy.com/jobs?search_keywords=CICD.md)[Data Governance](https://jobicy.com/jobs?search_keywords=Data%20Governance.md)[Data Observability](https://jobicy.com/jobs?search_keywords=Data%20Observability.md)[Apache Airflow](https://jobicy.com/jobs?search_keywords=Apache%20Airflow.md)[MLOps](https://jobicy.com/jobs?search_keywords=MLOps.md)[Data Warehouse Optimization](https://jobicy.com/jobs?search_keywords=Data%20Warehouse%20Optimization.md)[Technical Leadership](https://jobicy.com/jobs?search_keywords=Technical%20Leadership.md) Sample interview questionsHow would you design a layered Snowflake and dbt architecture that supports both trusted reporting and AI-driven use cases?I would establish clear staging, intermediate, and mart layers with documented ownership, contracts, lineage, and testing at each boundary. Curated marts and semantic definitions would expose stable business concepts, while governed access patterns, metadata, and freshness controls would make the platform dependable for BI, ML, and agent workflows. Describe your approach to reducing Snowflake cost while preserving performance for a growing analytics organization. I begin with workload profiling and query-history analysis to identify expensive patterns, inefficient models, and inappropriate warehouse sizing. I then prioritize model design improvements, incremental processing, query tuning, warehouse right-sizing and auto-suspend policies, and monitoring that assigns spend to domains and workloads. What DataOps controls would you introduce into a dbt-based analytics engineering workflow? I would use Git-based pull requests with automated dbt compilation, unit and schema tests, targeted build validation, SQL linting, and environment isolation. Production deployments should be gated, observable, reversible, and supported by secure secret management, data-quality alerts, and documented release procedures. How do you evaluate whether a new orchestration or ingestion tool is worth adopting? I define the operational problem and success criteria first, then run a focused proof of concept against representative pipelines. The evaluation considers reliability, extensibility, developer experience, security, observability, total cost, integration fit, migration complexity, and the team’s ability to operate the tool long term. How would you mentor analytical engineers while retaining high standards without becoming a delivery bottleneck? I would publish architectural principles and reusable patterns, hold lightweight design reviews for consequential changes, and provide actionable feedback through pairing and pull requests. Clear decision records, documentation, and delegated ownership help engineers make sound decisions independently while escalating only the highest-impact trade-offs. Opportunity details ## About this role. What Makes Cloudbeds Unique At [Cloudbeds](https://www.cloudbeds.com/), 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. Our Data Analytics & BI Team: 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. How You’ll Make an Impact: As our Tech Lead Data Engineer & Data Architect, you will shape the technical direction, architectural standards, and data governance of our core Data Analytics platform. This is a hands-on leadership role: you will act as our Data Architect—owning logical and physical data modeling, warehouse design, and layered curation (staging → intermediate → mart)—while serving as our Data Engineering Tech Lead. 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. What You Bring to the Team: * 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. What Sets You Up for Success: * 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. Bonus Skills to Stand Out: * Exposure to applied AI tooling (Snowflake Cortex, AI Classify, RAG workflows, LLM agents, MCP servers). * Familiarity with modern BI consumption tools like Sigma or workflow engines like n8n. Work Authorization: Please note that applicants must be currently authorized to work in the location where the position is located without requiring visa sponsorship. At this time, Cloudbeds is unable to provide sponsorship for work visas. What to Expect – Your Journey with Us Behind Cloudbeds’ revolutionary technology is a team redefining what’s possible in hospitality. We’re 650+ team members across 40+ countries, bringing together elite engineers, AI architects, world-class designers, hoteliers, and hospitality veterans to solve challenges others haven’t dared to tackle. Our diverse team speaks 30+ languages, but we all share one language: a passion for innovation and travel. From pioneering breakthroughs in machine learning to revolutionizing how hotels operate, we’re not just watching the future of hospitality unfold – we’re coding it, designing it, writing it, and shipping it. If you’re ready to work alongside some of the brightest minds in tech who are obsessed with using AI to transform a trillion-dollar industry, this is your chance to be part of something extraordinary. Learn more online at [cloudbeds.com](https://www.cloudbeds.com/) Cloudbeds [Awards](https://www.cloudbeds.com/press/?press_type=company-news&news_category=awards): * Winner – Hotel Management Software | HotelTechAwards (2026) * Overall 10 Best Places to Work | HotelTechAwards (2026) * Overall Top 10 Hotelier’s Choice | HotelTechAwards (2026) * Finalist – Property Management Systems (PMS) & Channel Managers | HotelTechAwards (2026) * Most Loved Workplace® Certified (2024) * Deloitte Technology Fast 500 (2024) Discover our Benefits: * Remote First, Remote Always * PTO in accordance with local labor requirements * Fully Paid Parental Leave * Home office stipend based on country of residency * Professional development courses in Cloudbeds University * Access to professional development, including manager training, upskilling, and knowledge transfer Everyone is Welcome – A Culture of Inclusion 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. As part of this commitment, Cloudbeds will ensure that persons with disabilities are provided reasonable accommodations in the hiring process. We encourage deaf, hard-of-hearing, deaf-blind, and deaf-disabled individuals to apply. If reasonable accommodation is needed to participate in the job application or interview process or to perform essential job functions, please contact our HR team by phone at (858) 201-7832 or via email at accommodations@cloudbeds.com. Cloudbeds will provide an American Sign Language (ASL) interpreter where needed as a reasonable accommodation for the hiring process. To all Staffing and Recruiting Agencies: Our Careers Site is only for individuals seeking a job at Cloudbeds. Staffing, recruiting agencies, and individuals being represented by an agency are not authorized to use this site or to submit applications, and any such submissions will be considered unsolicited. Cloudbeds does not accept unsolicited resumes or applications from agencies. Please do not forward resumes to our jobs alias, Cloudbeds employees, or any other company location. Cloudbeds is not responsible for any fees related to unsolicited resumes/applications. Show more [Apply now >](https://jobicy.com/jobs/156345-tech-lead-data-engineer.md) > Annual salary information is not provided for this position. 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