Senior Data Platform Engineer

Lead Data & AI Platform Engineer

At Order

YOYO, 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.

Competitive salary, growing international company, and growth opportunities.

Your mission is to lead the continuity, modernisation, and AI‑enablement of Order

YOYO’s data platform during a critical scaling phase.

• Lead the architecture and evolution of Order
  
  YOYO’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.

• 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”…