At OrderYOYO

At OrderYOYO, data powers executive reporting, payments, finance, merchant insights, product analytics, AI, marketing automation, operational decision-making, and M&A integration.

We are looking for a Lead Data & AI Platform Engineer

This is a hands-on technical leadership role for a strong engineer who can build reliable data systems, modernise our data lake, automate data pipelines, and pioneer the practical use of AI across data engineering, reporting, analytics, and business insight generation.

You will play a central role in making OrderYOYO a more data-driven and AI-enabled company.

Competitive salary, growing international company, and growth opportunities.

Role mission

Your mission is to lead the continuity, modernisation, and AI-enablement of OrderYOYO’s data platform during a critical scaling phase.

Core Responsibilities

• Lead the architecture and evolution of OrderYOYO’s Microsoft Fabric platform across lakehouse, warehouse, notebooks, pipelines, semantic models, Power BI, and governance.
• Make Fabric the trusted source of truth for priority business metrics and reporting.
• Drive migration from legacy reporting and fragmented metric tooling into governed semantic models.
• Build and improve production-grade data pipelines across APIs, files, events, CRM systems, payment platforms, operational databases, and acquired-company data sources.
• Use AI and automation to accelerate ETL/ELT development, data mapping, documentation, testing, report generation, monitoring, and data-quality management.
• Design reusable semantic models, DAX measures, and governed metric definitions for leadership, finance, commercial, product, marketing, payments, support, and operations.
• Build automated reporting and insight-generation capabilities that reduce manual analysis and improve decision speed.
• Establish robust orchestration, monitoring, alerting, lineage, data-quality checks, and incident-response processes.
• Support CRM and operational data integrations, including identity mapping, schema mapping, outbound data feeds, reverse-ETL patterns, and monitoring.
• Create repeatable ingestion and modelling patterns for acquired businesses, making future integrations faster, cleaner, and more auditable.
• Define engineering standards for data pipelines, notebooks, semantic models, documentation, code review, testing, release management, and runbooks.
• Lead and mentor data engineers, analytics engineers, BI analysts, data scientists, and ML/AI practitioners.
• Partner with business stakeholders to turn ambiguous questions into reliable metrics, trusted reports, and scalable data products.
• Ensure data and AI solutions are secure, privacy-conscious, auditable, and aligned with GDPR and internal governance requirements.