Analytics / Data Engineer

**Prop Firm Match Global FZCO** is a leading platform for discovering, comparing, and selecting top proprietary trading firms. We provide traders with tools and features to easily compare challenge details, read verified reviews, see accurate payout data, and much more. We're a fast\-moving, fully remote team with members from all around the world caring deeply about the quality of what we build. Our culture values ownership, clear communication, and practical impact over fluff. Whether you're a trader, technologist, marketer, or operator \- your work here shapes how thousands of users find and trust prop firms. **About the department** Data \& Analytics is the function that turns raw business data into decisions. We own the data platform, product analytics, marketing attribution, and revenue operations analytics. Our current priority is building reliable data infrastructure that supports both day\-to\-day reporting and the next wave of AI\-driven workflows at PFM. **About the role** Own and scale the core data platform that powers reporting, attribution, product analytics, reconciliation, AI workflows, and company\-wide decision\-making at PFM. Turn fragmented raw data into reliable, reusable business intelligence layers that enable faster decisions and scalable automation. **Performance objectives** *Objective 1 \- Own the data platform foundation* Outcome: Reliable, well\-modelled, documented warehouse data that powers reporting and AI workflows across the company. Typical tasks: * Own BigQuery warehouse architecture and dbt models. * Manage Airbyte pipelines and tracking infrastructure. * Apply orchestration tools (e.g., Airflow) for recurring workflows. * Monitor pipeline reliability and address breakages. *Objective 2 \- Build reliable business data models* Outcome: Trusted source of truth across revenue, attribution, product funnels, partner performance, finance, and executive reporting. Typical tasks: * Model revenue and commission flows end\-to\-end. * Model product funnels for conversion analysis. * Model partner performance for the Revenue Operations team. * Build executive reporting layers. *Objective 3 \- Improve data quality and reduce manual work* Outcome: Reduced reconciliation discrepancies; reduced manual reporting overhead across Finance, Analytics, and Partners teams. Typical tasks: * Implement governance, permissions, monitoring, and alerting. * Automate recurring reports and reconciliations. * Enable self\-service analytics for non\-technical stakeholders. * Improve documentation and discoverability of warehouse tables. *Objective 4 \- Enable AI workflows and new integrations* Outcome: AI/automation initiatives scale on clean data; new sources are integrated without manual stitching. Typical tasks: * Integrate QuickBooks, CRM, support, social, and operational systems. * Support AI enablement (Claude / internal AI assistants) with structured data. * Use AI\-assisted coding workflows to speed d