Lead Data & AI Platform Engineer — Scale a Modern Data Lake

OrderYOYO

Københavns Kommune

On-site

DKK 900,000 - 1,400,000

Full time

14 days+

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Job summary

OrderYOYO is seeking 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, leading continuity and

Qualifications

Strong experience in modern data platform engineering, analytics engineering, data warehousing, or data architecture.
Proven leadership of complex data-platform work in SaaS/fintech/e-commerce contexts.
Expert SQL/T-SQL skills with production-grade pipelines.
Strong Python or PySpark for maintainable data pipelines.
Power BI and DAX with semantic modelling and governance.
Experience applying AI/automation to data engineering and reporting.

Responsibilities

Lead architecture and evolution of OrderYOYO’s data platform across lakehouse, warehouse, notebooks, pipelines, semantic models, Power BI, and governance.
Make Fabric the trusted source of truth for key metrics and reporting.
Drive migration to governed semantic models and modern tooling.
Build production-grade data pipelines across APIs, files, events, CRMs, payments, and acquired data.
Use AI/automation to accelerate ETL/ELT, data mapping, testing, monitoring, and data quality.
Design reusable semantic models, DAX measures, and governed metric definitions for leadership and teams.
Automate reporting and insight generation to speed decision making.
Establish orchestration, monitoring, lineage, data-quality checks, and incident response.
Support CRM/operational data integrations, identity/schema mapping, and reverse-ETL patterns.
Create repeatable ingestion patterns for acquired businesses and future integrations.
Define engineering standards for pipelines, notebooks, taxonomy, code review, testing, and runbooks.
Lead and mentor data engineers, analytics engineers, BI analysts, data scientists, and ML/AI practitioners.
Collaborate with stakeholders to turn questions into reliable metrics and scalable data products.
Ensure GDPR-compliant, secure, auditable AI/data solutions.