Tech Lead Data Engineer

Role overview This senior, remote-first position sets the technical direction for a hospitality-industry analytics platform that ingests operational data from properties across many countries. The role blends hands-on data architecture with engineering leadership, shaping how a small, senior team models data, governs access, and prepares the platform for AI-driven workflows.

Responsibilities - Define and evolve the cloud data architecture and dimensional modeling approach (star schemas, slowly changing dimensions, conformed dimensions), documenting key choices through Architecture Decision Records. - Lead end-to-end pipeline design covering ingestion, API integrations, custom connectors, and orchestrated ELT/ETL processes. - Strengthen DataOps practices by embedding data-specific discipline into existing Git, CI/CD, and ticketing workflows, including automated testing on pull requests, environment isolation, and credential management. - Build out governance, cataloging, and observability foundations to track lineage, freshness, completeness, and automate failure alerting. - Continuously tune warehouse query performance, clustering, and sizing to balance execution speed and compute cost. - Mentor a team of analytical engineers through architectural guidance, design reviews, and agile planning.

Requirements - Senior hands-on experience as a Data Engineer, Data Architect, or Tech Lead in cloud data environments. - Deep expertise with a modern cloud warehouse (such as Snowflake) and with dbt for transformation and testing. - Strong background in pipeline orchestration, ELT/ETL design, and API-based data ingestion. - Familiarity with DataOps, CI/CD, and Git-based development workflows applied to data pipelines. - Ability to set platform-level standards, document decisions, and raise the technical bar of a small distributed team.

Nice to have - Experience driving MLOps workflows or building semantic layers that support AI agents and automated use cases. - Exposure to orchestration and BI tooling such as Matillion and Sigma, or comparable platforms. - Background collaborating closely with data science, infrastructure, and business stakeholders.

Benefits and work setup - Fully remote, remote-first organization. - Paid time off aligned with local labor requirements. - Fully paid parental leave. - Home office stipend sized to country of residency. - Access to structured professional development including courses, manager training, and ongoing upskilling.