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Groupe SEGULA Technologies SA seeks a Lead Product Development Data Engineer to bridge engineering and enterprise teams. You will own data workflows, cloud architectures, and scalable analytics to enable informed decision-making across IT, Manufacturing, Finance, and Supply Chain.
You will design and optimize data pipelines using Snowflake, Databricks, and Microsoft Fabric while advancing governance, security, and lineage across Lakehouse environments.
The Lead Product Development Data Engineer owns key data processes and analytics applications within the broader Product Development Process (PDP). In this cross-functional role, you will bridge the gap between Engineering and enterprise organizations—including IT, Manufacturing, Finance, Purchasing, and Supply Chain—to establish standardized, high-performing data workflows, modern cloud architectures, and scalable analytics solutions.
Enterprise Platform Management: Build, optimize, and maintain big data pipelines and architectures leveraging Snowflake, Databricks, or Microsoft Fabric.
Cloud & Data Pipelines: Design scalable data models and storage solutions (Lakehouse, DirectLake) using SQL, Python, PySpark, and cloud ecosystems across Azure or AWS.
Modern Analytics Integration: Migrate legacy data workflows to modern cloud architectures, establishing robust data governance, pipeline security, and data lineage.
Process Ownership: Drive global uniformity, documentation, and continuous refinement across assigned Product Development processes and digital applications.
Business Intelligence & Dashboards: Develop, deploy, and maintain executive-level dashboards and reporting tools using Power BI, PowerApps, and Palantir Foundry.
Workflow Automation: Streamline administrative and operational cross-functional engineering tasks using Power Automate and Microsoft 365 tools.
Stakeholder Alignment: Collaborate across IT, Manufacturing, Finance, Purchasing, and Program Management to capture technical requirements and align business goals.
AI/ML Workflows: Support the integration of machine learning models and predictive analytics into existing engineering data pipelines.
Executive Communication: Translate complex data technicalities and process trade-offs into clear decision packages, business cases, and leadership presentations.