Senior Data Engineer (Azure & Databricks) at Socotec

As a Senior Data Engineer, you will design and build scalable data systems that power SOCOTEC’s digital platforms and enterprise analytics. You will play a key role in architecting our modern data stack, enabling reliable ingestion, transformation, and modeling of data across multiple enterprise systems. This role requires strong experience in Databricks, distributed data processing, and enterprise data architecture, along with the ability to lead large data initiatives and collaborate with engineering and business teams. You will work closely with software engineers and platform teams to ensure SOCOTEC’s data ecosystem is reliable, scalable, and optimized for both operational systems and advanced analytics. Key Responsibilities Build SOCOTEC’s Enterprise Data Platform • Design and implement scalable data pipelines that ingest and transform data from enterprise systems such as ERP, CRM, and operational databases. • Develop and maintain data pipelines in Databricks using Spark and Delta Lake. • Build and maintain data models that support analytics, AI applications, and operational systems.Lead Master Data Management (MDM) Initiatives • Architect and implement SOCOTEC’s custom MDM platform using Databricks. • Design data models that establish consistent “golden records” across multiple enterprise systems. • Implement data governance, lineage, and quality frameworks.Design Enterprise-Scale Data Pipelines • Build reliable ingestion pipelines for large-scale structured and semi-structured data. • Implement batch and near-real-time data processing pipelines. • Optimize large-scale data processing jobs for performance and cost efficiency.Enable Data for AI and Advanced Analytics • Partner with AI and software engineering teams to deliver high-quality datasets for machine learning and AI applications. • Build data pipelines that support SOCOTEC’s digital products, analytics dashboards, and operational platforms.Drive Data Architecture and Best Practices • Define standards for data modeling, pipeline design, and data quality. • Implement monitoring, observability, and alerting data systems. • Ensure scalability, reliability, and security across the data platform.