Senior/Lead Azure Cloud Data Engineer - Permanent

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)