Lead Data Platform & AI Engineer

OrderYOYO

Amsterdam

On-site

EUR 90,000 - 150,000

Full time

7 days ago

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Job summary

OrderYOYO is seeking a senior data platform engineer to govern and evolve a modern data foundation. You will migrate legacy reporting to a Microsoft Fabric-based semantic layer, design Power BI models, and lead data pipelines across CRM, product, marketing, and finance.

You will mentor engineers, drive automation, and ensure reliable, auditable data delivery. The role emphasizes AI-enabled data engineering, stakeholder collaboration, and governance for scalable analytics across multiple business

Qualifications

6+ years in modern data warehousing, analytics engineering or data platform engineering.
Strong Microsoft Fabric capability, or deep Azure Synapse / Databricks experience with clear ability to specialise quickly in Fabric.
Expert SQL/T-SQL plus strong Python or PySpark, with a track record of building maintainable ELT/ETL pipelines and analytical data models.
Strong Power BI and DAX experience, including semantic modelling, incremental refresh, performance tuning, model governance and capacity/cost awareness.
Experience leading legacy-to-modern data platform migrations, including metric parity, stakeholder validation, change control and safe decommissioning.
Experience operating production data systems: monitoring, alert design, incident triage, root-cause analysis, data-quality checks, lineage and runbooks.
Comfortable with Git-based data engineering workflows, pull requests, release discipline and standards for notebooks, pipelines and semantic model changes.
Practical experience using AI or automation to improve data engineering, reporting, documentation, testing, monitoring, migration or developer productivity.

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.
Use AI and automation to accelerate ETL/ELT development, data mapping, documentation, testing, report generation, monitoring and data-quality management.
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.
Mentor engineers and analysts and translate business-critical data needs into pragmatic technical delivery.
Build automated reporting and insight-generation capabilities that reduce manual analysis and improve decision speed.