Senior Data & AI Platform Engineer

OrderYOYO

. Design, build and operate production-grade data solutions in Microsoft Fabric across Lakehouse, Warehouse, notebooks, pipelines and related platform capabilities .

Posted 11/5/2026full-timeRemote • GermanySenior💰 £80,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates extensive experience in designing and operating production-grade data solutions using Microsoft Fabric, with a strong focus on data engineering, ingestion patterns, and AI integration. Proven ability to lead migrations from legacy systems while ensuring data quality and performance optimization.

Highest-signal resume keywords

Microsoft Fabric ExpertiseProduction-Grade PythonExpert SQL and T-SQLData Engineering Problem SolvingAI Engineering Experience

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Data EngineeringIncremental Data IngestionLakehouse ArchitectureAnalytical Data ModellingSemantic Data ModellingPythonPySparkSQLT-SQLInfrastructure-as-Code

Soft Skills

Stakeholder ManagementProblem SolvingCollaborationLeadership

Tools & Technologies

Microsoft FabricPower BIAzure SQLADLS/OneLakeTerraformBicepAzure OpenAI

Industry Keywords

SaaSFintechE-CommerceData ScienceMachine Learning

Tech Stack

Tools & technologies

AzurePySparkPythonSparkSQLTerraformVault

About the role

Key responsibilities & impact

Design, build and operate production-grade data solutions in Microsoft Fabric across Lakehouse, Warehouse, notebooks, pipelines and related platform capabilities
Build and run production pipelines in Fabric, Python and PySpark
Design ingestion and transformation patterns across multiple source systems, databases and business domains
Manage Fabric capacity and cost, and retire redundant legacy layers
Lead migrations from legacy to modern platforms with metric parity, stakeholder sign-off, change control and safe decommissioning
Run the platform in production through monitoring, alerting, incident triage, root-cause analysis, data-quality checks, lineage and runbooks
Diagnose and resolve production issues across SQL, Spark, pipelines and semantic models
Support and evolve governed semantic models and reusable metric definitions with analytics and business stakeholders
Build AI into the platform and delivery using RAG, Azure OpenAI and agents, with prompt and version management
Deliver applied data science such as churn, recommendations, personalisation, fraud or risk models, and show the results
Work with executives, Finance, Product and engineering to make platform and delivery decisions

Requirements

What you’ll need

6+ years of relevant data experience, including substantial recent hands-on data engineering or data-platform engineering responsibility in production environments
Demonstrated ability to independently take a data-engineering problem from requirements and technical design through implementation, production deployment, monitoring and ongoing operation
Deep hands-on Microsoft Fabric, or strong Synapse, Databricks or Delta experience with a clear path to Fabric
Strong understanding of lakehouse architecture and Delta-based data engineering, including partitioning, schema evolution, file optimisation and performance considerations
Expert SQL and T-SQL, including complex modelling and tuning
Production-grade Python or PySpark
Strong experience designing and implementing incremental data ingestion patterns, including approaches such as CDC, change tracking or watermarks, with reliable merge/upsert, replay and idempotent processing
Experience with analytical and semantic data modelling, ideally with Power BI/Fabric semantic models, including performance optimisation, governance and reusable business metrics
Working knowledge of Azure platform fundamentals including Azure SQL, ADLS/OneLake, Entra ID, managed identities, RBAC and Key Vault
A track record of leading a legacy-to-modern migration
Real, hands-on AI engineering beyond out-of-the-box Copilot
Concrete data science or ML models you delivered, with results
Strong Power BI/Fabric experience, including Direct Lake, DAX, semantic model performance tuning, incremental refresh and capacity considerations
Infrastructure-as-Code experience with Terraform, Bicep or equivalent tooling
Data platform experience in SaaS, marketplace, fintech, payments, e-commerce or multi-region businesses

Benefits

Comp & perks

Quarterly team gatherings in one of our global offices
A remote role
Ownership of a business-critical data and AI platform
A collaborative, international engineering team