teamtailor
Senior Data Platform Engineer
OrderYOYO
Remote, GBR
Full-time
Senior · 6+ years experience
Remote
Discovered 2 weeks ago
Microsoft FabricAzure SynapseDatabricksSQLPythonPySpark
Free
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Microsoft FabricAzure SynapseDatabricks
Key Skills for This Role
Microsoft FabricAzure SynapseDatabricksSQLPythonPySpark
Full Job Posting
Senior Data Platform Engineer
At OrderYOYO, data powers executive reporting, payments, finance, merchant insights, product analytics, AI, and M&A integration. This role will shape the governed data foundation that supports our next stage of scale.
Competitive salary, growing international company, and growth opportunities.
Role mission
Own the continuity and evolution of OrderYOYO’s modern data platform during a critical scaling phase. You will lead the migration from legacy reporting and metric tooling into a governed Microsoft Fabric platform, keep business-critical BI and semantic models reliable, improve data pipeline stability and monitoring, support CRM data integration, and provide senior technical leadership for data engineering delivery.
Core responsibilities
- Lead hands-on Microsoft Fabric architecture across lakehouse, warehouse, notebooks, semantic models, Git-backed delivery and production governance.
- Drive migration from legacy reporting and metric tooling into a governed Fabric semantic layer, including parity testing, stakeholder sign-off and safe decommissioning.
- Own and improve data pipelines across APIs, files, events and operational stores; establish robust orchestration, monitoring, alerting, data-quality checks and incident response.
- Design high-quality Power BI semantic models, DAX measures and reusable metric definitions for leadership, finance, commercial, product, marketing, payments and support reporting.
- Support CRM and operational data integrations, including outbound data feeds, identity mapping, schema mapping, reverse-ETL patterns and monitoring.
- Create reliable ingestion and modelling patterns for acquired businesses, so future integrations are repeatable, auditable and faster to execute.
- Set data-engineering standards: definition of ready/done, code review, release discipline, documentation, runbooks and platform change governance.
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