data engineer for KPI reporting

Camlin is a global technology company that develops products and services for industries including power and rail, while also participating in research and development projects across scientific sectors. It operates in more than 20 countries and develops SaaS platforms, internal systems, and mechatronics-based products.

• Design, build, and maintain scalable data pipelines that ingest, transform, and integrate data from multiple business and engineering systems;
• Connect fragmented source systems into a reliable and governed data foundation for KPI measurement, reporting, and analytics;
• Develop ETL/ELT processes using SQL, Python, Databricks, and related technologies;
• Create and maintain analytics-ready data models for trusted, reusable reporting across the Technology Division;
• Enable KPI implementation through consistently defined, traceable, and automatically calculated measures where possible;
• Translate metric definitions into scalable technical models and ensure semantic consistency across domains;
• Build and evolve data platform capabilities using Databricks or equivalent modern data platform technologies;
• Design data structures and transformation layers for reporting, self-service analytics, and future advanced analytics use cases;
• Implement data quality controls, validation checks, and reconciliation processes;
• Support data governance practices including lineage, traceability, consistency, and controlled access;
• Identify and resolve data issues across upstream and downstream systems;
• Connect data across requirements, design, build, test, deployment, support, and product performance toolchains;
• Integrate data from ALM/PLM platforms, CI/CD systems, telemetry/IoT feeds, operational applications, and other systems of record;
• Help create a joined-up view of engineering flow, delivery performance, reliability, quality, and lifecycle signals across hardware and software environments;
• Translate business and reporting requirements into reliable datasets, pipeline enhancements, and reusable data assets;
• Communicate technical constraints, trade-offs, and opportunities to technical and non-technical stakeholders.

• Experience in senior data engineering or closely related roles;
• Strong SQL skills for querying, transformation, and performance optimization;
• Strong Python skills for data engineering, automation, and integration;
• Experience with Databricks or an equivalent modern data platform;
• Experience building and maintaining ETL/ELT pipelines;
• Experience with data modelling for analytics and reporting;
• Experience with version control and disciplined development practices;
• Strong understanding of data integration across multiple source systems;
• Strong understanding of trusted data foundations for analytics and KPI reporting;
• Strong understanding of data quality, lineage, governance, and reliability practices;
• Experience working with complex, multi-source datasets in operational and engineering environments;
• Strong stakeholder engagement skills and ability to translate requirements into practical technical solutions;
• Nice to have: familiarity with mechatronics environments, exposure to PLM/ALM ecosystems and engineering toolchains, exposure to IoT/telemetry and product performance data, experience supporting Power BI or other BI/reporting consumption layers, knowledge of Continuous Improvement methods.

• No conditions specified