AI Data Engineer

Vous êtes un ingénieur de données IA expérimenté et possédez une expertise pratique approfondie de Microsoft Fabric et de l'ingénierie de données Azure moderne ?

emagine vous offre l'opportunité de participer au développement, à l'optimisation et à la mise à l'échelle de pipelines de données et d'architectures lakehouse qui permettent la mise en ?uvre d'initiatives avancées en matière d'analyse et d'IA au sein d'une équipe internationale.

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 & Experience10+ 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 & DeliverablesYou will play a pivotal role in designing and running data engineering capabilities that enable analytics, AI, and ML use cases. Responsibilities include:

Data Engineering & ArchitectureDesign, 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 IntegrationCollaborate 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 & SecurityImplement 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 EngagementWork 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.

Profil candidat:

Plus de 10 ans d'expérience en tant qu'ingénieur de données, ingénieur en intelligence artificielle ou dans un poste similaire. Solide expérience pratique de Microsoft Fabric (Lakehouse, ingénierie des données, Data Factory, pipelines de données). Expertise approfondie des services de données Azure : Azure Data Factory Azure Databricks / Spark Azure Synapse Analytics (pools SQL) Azure Storage / ADLS Gen2 Azure Functions Azure Machine Learning Maîtrise de Python et SQL pour le traitement et la transformation des données. Solide expérience dans la création d'architectures lakehouse et de pipelines Delta Lake. Connaissance des pratiques DevOps/MLOps modernes, notamment les tests, le contrôle de version et le déploiement automatisé. Connaissance des solutions de gouvernance telles qu'Azure Purview ou Fabric governance.