Fabric Data Engineer

Xebia Group; Netherlands; Remote; EUR 70,000 - 100,000; Full time

Benefits offered by this job

Knowledge sharing culture

€5k annual learning budget

All-in-one roof for knowledge sharing

Team events and activities

Job summary

Xebia Group's Xebia Microsoft Services helps organizations turn their data ambitions into reality through modern engineering, thoughtful architecture, and deep Microsoft expertise. As a Data Engineer, you design and build the data solutions that enable cloud-native analytics and guide clients in adopting Microsoft Fabric.

You will lead in Fabric components, design end-to-end data architectures, build pipelines with Spark and Data Factory, ensure governance, and enable BI with semantic models and

Qualifications

• 4–8+ years of experience in data engineering, cloud analytics, or consultancy roles.
• Strong hands-on experience with Microsoft Fabric components: OneLake, Lakehouse, Spark, Data Factory, KQL/Real-Time Analytics, and Power BI.
• Solid data engineering skills in Python, SQL, Spark, Delta Lake, and distributed compute.
• Familiarity with the Azure data ecosystem (ADLS, EventHub, Synapse, Azure Functions, Entra ID, security principles).
• Understanding of governance practices: metadata, lineage, quality frameworks, domain-driven design.
• Relevant Microsoft certifications (DP-600, DP-203, AZ-305, PL-300) are a plus.

Responsibilities

• Solution design & architecture: end-to-end data architectures using Microsoft Fabric components.
• Data engineering: build and optimize data pipelines and notebooks.
• Data governance: define governance frameworks, security, lineage, and cataloging.
• Analytics & BI enablement: create semantic models and datasets for BI and real-time analytics.
• Azure integration: connect Fabric with ADLS, Event Hub, Azure SQL, Databricks, Purview.
• Stakeholder enablement: workshops, PoCs, and knowledge transfer.

Skills

Microsoft Fabric

Python

SQL

Spark

Delta Lake

Power BI

OneLake

Data Factory

Tools

Azure Data Factory

Databricks

Power BI

Job description

At Xebia Microsoft Services (XMS), we help organizations turn their data ambitions into reality through modern engineering, thoughtful architecture, and deep Microsoft expertise. As a Data Engineer, you design and build the data solutions that make this possible. You bring clarity to complex environments, guide clients as they adopt Microsoft Fabric, and support teams in working effectively with cloud-native analytics. Join us and help shape the next generation of data platforms.

Responsabilities

As a Data Engineer, you help clients turn their data ambitions into reliable, scalable data platforms. Within this role, you will also take a leading position in Microsoft Fabric, guiding both clients and internal teams in adopting and implementing Fabric as a modern data platform. You work across architecture, engineering, analytics, and governance, bringing technical depth and practical guidance to every stage of the engagement. You ensure that platforms built on Fabric are well-designed, maintainable, and ready to support real business value.

In practice, this means:

• Solution Design & Architecture: Designing end-to-end data architectures using Microsoft Fabric (Lakehouse, Data Engineering, Data Factory, Real-Time Analytics, Power BI).

• Data Engineering: Building and optimizing data pipelines and notebooks using Spark, Dataflows Gen2, Pipelines, and ELT/ETL best practices.

• Data Governance: Defining governance frameworks: domains, workspaces, OneLake structures, cataloging, lineage, and security.

• Analytics & BI Enablement: Creating high-quality semantic models and datasets that empower BI teams, and real-time analytics use cases.

• Azure Integration: Integrating Fabric with broader Azure services such as ADLS, Event Hub, Azure SQL, Databricks, and Purview.

• Stakeholder Enablement: Guiding stakeholders through Fabric adoption with workshops, assessments, PoCs, and knowledge transfer.

You combine deep technical expertise with a consultant mindset. You enjoy navigating complexity, aligning stakeholders, and delivering solutions that balance architectural integrity with practical execution. Your communication is clear, your thinking is structured, and you’re excited about working with cutting-edge Microsoft data technologies.

This includes:

• 4–8+ years of experience in data engineering, cloud analytics, or consultancy roles

• Strong hands-on experience with Microsoft Fabric components: OneLake, Lakehouse, Spark, Data Factory, KQL/Real-Time Analytics, and Power BI.

• Solid data engineering skills in Python, SQL, Spark, Delta Lake, and distributed compute.

• Familiarity with the Azure data ecosystem (ADLS, EventHub, Synapse, Azure Functions, Entra ID, security principles).

• Understanding of governance practices: metadata, lineage, quality frameworks, domain-driven design.

• Relevant Microsoft certifications (DP-600, DP-203, AZ-305, PL-300) are a plus.
  

Your Path to Success in Your First Year

• Deliver high-impact Fabric solutions by combining strong technical execution with clear architectural direction.

• Build trusted advisory relationships with clients by shaping their Fabric roadmap and guiding modernization decisions.

• Strengthen XMS delivery by contributing to best practices, reusable assets, and internal knowledge sharing.

• Work closely with architects, engineers, and fellow consultants to deepen your expertise in Fabric and the Azure data ecosystem.
  

Why Work for Xebia Microsoft Services

• Knowledge sharing at the core: Weekly XKEs, quarterly Innovation Days, and colleagues who genuinely enjoy sharing and learning from each other.

• Broadening your expertise: €5k annual learning budget, unlimited training access, and colleagues across cloud, data, AI, and security.

• Everything under one roof: Knowledge sharing, challenging projects, growth opportunities, and plenty of humor – daily.

• Making memories together: Annual ski trips (Skibia), summer getaways (Scubia), and a wide variety of activities from motorcycling and hockey to cooking workshops and karaoke.