Tech Lead Data Engineer

𝗪𝗵𝗮𝘁 𝗠𝗮𝗸𝗲𝘀 𝗖𝗹𝗼𝘂𝗱𝗯𝗲𝗱𝘀 𝗨𝗻𝗶𝗾𝘂𝗲 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. 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 Air