Role: Data & Platform Engineering ( Azure, Python)
Duration: 2 Months
Work Timings: French Timings
Years of Experience: 5+ years
Key Responsibilities
- Design and develop scalable data and analytics solutions using Microsoft Fabric and Azure
services
- Build and maintain data pipelines using Python, Fabric Data Engineering, Lakehouse,
Notebooks, and Pipelines
- Integrate analytical and ML outputs into downstream analytics platforms and dashboards
- Implement data transformations, validation checks, and performance optimisations
Predictive Modeling & Analytics
- Design, build, and iterate on predictive and analytical models using Python
- Develop models and scoring logic that support real-world decision-making, with an emphasis
on interpretability and stability
- Produce explainable outputs, including scores, categories, and key drivers influencing model
results
- Perform diagnostics and validation to understand model behaviour across different data
segment.
Decision Support & Scenario Analysis
- Translate business rules, constraints, and policies into quantitative logic that can be applied
consistently
- Build what-if and scenario analyses to evaluate trade-offs and outcomes under changing
assumptions
- Clearly communicate model behaviour, limitations, and trade-offs to non-technical
stakeholders
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Collaboration & Governance
- Work closely with business stakeholders to understand decision processes and analytical
needs
- Collaborate with platform and architecture teams to productionize solutions responsibly
- Contribute to technical design discussions and ensure adherence to security, governance, and
compliance standards
- Troubleshoot production issues and continuously improve solution reliability and clarity
Required Skills
- Solid experience with Microsoft Azure, including services such as Azure Data Factory /
Synapse / Azure Functions, Azure Data Lake Storage (ADLS), Azure SQL (Cosmos DB is
a plus)
- Hands-on experience with Microsoft Fabric, including Data Engineering, Lakehouses,
Notebooks, and Pipelines
- Understanding of data modeling, ETL/ELT patterns, and analytics workloads
- Strong proficiency in Python, including data processing, feature engineering, and ML
workflows
- Experience building predictive or analytical models using libraries such as scikit-learn,
pandas, NumPy
- Experience with Git, CI/CD pipelines, and DevOps practices
- Ability to explain analytical concepts and results clearly to non-technical stakeholders
Good to Have
- Experience with model interpretability or explainability techniques
- Exposure to scenario analysis, simulation, or decision-support systems
- Experience with Azure Machine Learning or ML deployment
- Knowledge of Power BI and semantic models in Fabric
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- Understanding of data quality, bias, or fairness considerations
- Familiarity with MLOps concepts
