data engineer for KPI analytics

Camlin is a global technology company that develops products and services for power and rail industries and conducts research and development projects across scientific sectors. It operates in over 20 countries and focuses on solving complex problems through engineering and design.

• 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 by structuring data for consistent, traceable, and automated measurement;
• Partner with the Technology Performance Analyst and business stakeholders to translate metric definitions into scalable technical models;
• Ensure semantic consistency across domains, teams, products, and reporting views;
• 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 covering lineage, traceability, consistency, and controlled access;
• Identify and resolve data issues across upstream and downstream systems with source-system owners;
• 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;
• Create a joined-up view of engineering flow, delivery performance, reliability, quality, and lifecycle signals across hardware and software environments;
• Partner with cross-functional engineering, product, security, data, and applied science teams to understand source systems, data meaning, and business context;
• 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.

• Strong experience with SQL for querying, transformation, and performance optimization;
• Strong experience with Python for data engineering, automation, and integration;
• Strong 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;
• Experience in senior data engineering or closely related roles;
• 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;
• Ability to gather requirements and translate them 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.

• Office location: Belgrade, Serbia.