Les missions du poste
Important information
Contract type: Permanent contract
Salary: Salary according to profile
Location: Paris, France
Starting date:
2 to 4 weeks
Work mode: Hybrid
Published on: 22 June 2026
What they need
Role Overview
The Group has established a modern Azure-based Data Platform (Synapse, Data Factory, Data Lake, Power BI) to support enterprise-wide analytics and reporting. Following significant business expansion, the platform must now evolve into a more scalable, industrialized, and resilient data foundation, enabling advanced analytics and preparing for future AI use cases.
The Data function operates with a hybrid delivery model, combining a lean internal team and external partners. While the internal team defines the data strategy, governance, and platform vision, a significant part of the current build and run activities is supported by external partners (AMS and project-based delivery).
In this context, we are seeking a Senior / Lead Azure Data Engineer to progressively take ownership of the Group Data Platform. The role combines hands-on engineering, platform architecture, and delivery leadership, with a strong focus on reinforcing internal capabilities and reducing dependency on external partners over time.
The successful candidate will play a key role in:
- Driving the evolution of the platform toward a scalable, standardized, and high-performing architecture
- Strengthening internal technical ownership and structuring critical knowledge
- Challenging external partners and improving delivery quality and efficiency
- Contributing to the build-up of a more autonomous and scalable internal data team
This is a hands-on and transformational role, requiring the ability to operate in a partially outsourced environment while driving long-term platform industrialization, reliability, and ownership.
Key Responsibilities
1. Data Platform Ownership & Architecture
- Own and drive the evolution of the Azure Data Platform, defining architecture standards, data models, and technical roadmap aligned with business needs and future analytics/AI ambitions
- Act as design authority, ensuring scalability, performance, cost-efficiency, and standardization across all entities and data domains
2. Hands-on Data Engineering & Platform Build
- Design, build, and optimize scalable data pipelines and data products in Azure (Data Factory, Synapse, Data Lake), setting engineering standards and reusable frameworks
- Take direct ownership of critical developments (complex pipelines, performance optimization, architectural components), while guiding and validating implementations delivered by partners
3. AMS & Delivery Management
- Steer and challenge the Application Management Services partner, ensuring quality of delivery, adherence to standards, and continuous improvement of services
- Define priorities, review deliverables, and drive knowledge transfer to progressively reduce dependency on external partners
4. DataOps, Governance & Platform Reliability
- Establish and enforce DataOps practices (CI/CD, testing, monitoring, alerting) to industrialize and stabilize the platform
- Ensure data governance, data quality, and security standards are consistently applied across the data ecosystem
Objectives & Key Results
Immediate Priorities
- Ramp up on the Azure Data Platform and secure knowledge transfer from current partners
- Assess and streamline existing data architecture, pipelines, and tooling
- Stabilize and strengthen the Group Datahub (Synapse, Data Lake, Power BI datasets)
- Support critical initiatives by improving data pipeline reliability and scalability
Mid-term Objectives
- Expand the Group Datahub with standardized and certified datasets across entities
- Improve performance and accessibility of data for analytics and reporting
- Industrialize data ingestion, transformation, and deployment processes
- Reduce time-to-data availability and improve platform resilience
Strategic Objectives
- Prepare the data platform for advanced analytics and future AI use cases
- Improve data quality, lineage, and accessibility across the organization
- Contribute to the evolution toward a modern, scalable data platform (e.g., Fabric, Databricks)
Profile wanted
Profile
Technical
- Strong hands-on experience with Azure data services (Azure Synapse Analytics, Data Factory, Data Lake Storage, Azure SQL)
- Proven experience designing and building scalable ETL/ELT pipelines
- Experience with distributed data processing frameworks (e.g., Spark) is a plus
- Solid understanding of data modeling (dimensional modeling, Data Vault, data products)
- Experience with DevOps practices (Git, CI/CD, Infrastructure as Code such as Bicep/Terraform) and good understanding of Power BI data models and dataset optimization
Functional
- Understanding of business processes (Finance, Commercial, Supply Chain), preferably in pharma, FMCG, or retail
- Ability to translate business requirements into scalable and maintainable data solutions
- Experience working with cross-functional stakeholders to support data-driven decision-making
Soft Skills
- Strong engineering mindset with focus on scalability, quality, and automation
- Hands-on, proactive, and solution-oriented
- Ability to challenge existing designs and drive continuous improvement
- Strong communication skills (English required, French preferred)