AI Data Engineer (Microsoft Fabric & Azure) (h/f)

Are you an experienced AI Data Engineer with deep hands‑on expertise in Microsoft Fabric and modern Azure data engineering?
emagine has an opportunity for you to support the development, optimisation, and scaling of data pipelines and lakehouse architectures that enable advanced analytics and AI initiatives across a global team.

Skills & Experience

• 10+ years’ experience as a Data Engineer, AI Engineer, or similar role.

• Strong hands‑on experience with Microsoft Fabric (Lakehouse, Data Engineering, Data Factory, Data Pipelines).

• Deep expertise across Azure Data Services:

  • Azure Data Factory

  • Azure Databricks / Spark

  • Azure Synapse Analytics (SQL Pools)

  • Azure Storage / ADLS Gen2

  • Azure Functions

  • Azure Machine Learning

• Proficiency in Python and SQL for data processing and transformation.

• Strong experience building lakehouse architectures and Delta Lake pipelines.

• Knowledge of modern DevOps/MLOps practices including testing, version control, and automated deployment.

• Familiarity with governance solutions such as Azure Purview or Fabric governance.

Key Tasks & Deliverables

You will play a pivotal role in designing and running data engineering capabilities that enable analytics, AI, and ML use cases. Responsibilities include:

Data Engineering & Architecture

• Design, build, and maintain scalable data pipelines on Microsoft Fabric (Data Factory, Dataflows, Lakehouse, Warehouse, OneLake).

• Develop ingestion, transformation, and orchestration workflows using Azure Data Factory, Azure Databricks, Azure Synapse, and Azure Functions.

• Implement and optimize Delta Lake architectures for both structured and unstructured datasets.

• Build reusable, production-grade data products supporting analytics, ML, and AI workloads.

AI & Advanced Analytics Integration

• Collaborate with Data Scientists and ML Engineers to operationalize models via Azure Machine Learning, Fabric Data Science, or Databricks.

• Prepare feature‑engineered datasets for machine learning workloads.

• Implement MLOps workflows including automated deployments, model monitoring, and pipeline observability.

Governance, Quality & Security

• Implement data quality, observability, monitoring, and lineage frameworks using Fabric and Azure Purview.

• Ensure compliance with governance, privacy, and security standards.

• Manage CI/CD pipelines and version‑controlled data code via Azure DevOps or GitHub.

Collaboration & Stakeholder Engagement

• Work closely with business analysts, product owners, and domain experts to define data requirements and deliver high‑value solutions.

• Maintain documentation of data models, sources, pipelines, and best practices.

• Provide technical leadership and mentoring across the engineering team.

Why join this project?

You’ll be joining a collaborative, enabling environment that values innovation while giving you autonomy to architect modern data solutions at scale.

Apply with your latest CV or reach out to Brendan to find out more.