Lead Data & AI Platform Engineer 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 to own the next stage of our data platform evolution. 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. Must-have requirements Strong experience in modern data platform engineering, analytics engineering, data warehousing, or data architecture. Proven experience leading complex data-platform work in a SaaS, marketplace, fintech, payments, e-commerce, B2B2C, or multi-region business. Strong Microsoft Fabric capability, or deep Azure Synapse, Databricks, Delta Lake, or lakehouse experience with the ability to specialise quickly in Fabric. Expert SQL/T-SQL skills. Strong Python or PySpark engineering capability, with experience building maintainable, tested, production-grade data pipelines. Strong Power BI and DAX experience, including semantic modelling, incremental refresh, performance tuning, model governance, and capacity/cost awareness. Practical experience using AI or automation to improve data engineering, reporting, documentation, testing, monitoring, migration, or developer productivity. Experience building or operating production data systems with monitoring, alerting, incident triage, root-cause analysis, data-quality checks, lineage, and runbooks. Experience leading legacy-to-modern data platform migrations, including metric parity, stakeholder validation, change control, and safe decommissioning. Experience leading, mentoring, or technically guiding data engineers, analytics engineers, BI analysts, data scientists, or ML engineers. Strong judgement on when to move fast, when to standardise, when to automate, and when to say “not yet” with evidence. Strong-to-have experience Experience with Azure OpenAI, LLMs, RAG, AI agents, prompt/version management, or AI-assisted development workflows. Experience building AI-generated reporting, natural-language analytics, business copilots, automated insight generation, or merchant/customer intelligence tools. Experience with churn prediction, recommendations, personalisation, marketing automation, fraud/risk analytics, payments analytics, or finance automation. CRM-side data flows and reverse-ETL patterns, especially HubSpot, Salesforce, Zendesk, or similar platforms. M&A or acquired-company data integrations, including schema discovery, data profiling, file/API ingestion, master-data mapping, migration QA, and reporting continuity. GA4, BigQuery export, Google Ads, SEM feeds, Segment, or other event and marketing analytics sources. Responsible AI and governance experience, including RBAC, PII handling, audit logs, human approval flows, explainability, and GDPR-conscious design. Apply now if you fulfill the above criteria, we look forward to hearing from you.