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описание
Camlin is a global technology company that develops products and services for industries including power and rail. It also participates in research and development projects across scientific sectors and operates in more than 20 countries.
задачи
Design, build, and maintain scalable data pipelines that ingest, transform, and integrate data from business and engineering systems
Connect fragmented source systems into a reliable, 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
Structure data to support consistently defined, traceable, and automatically calculated KPIs
Partner with the Technology Performance Analyst and business stakeholders to translate metric definitions into scalable technical models and maintain semantic consistency across teams, products, and reporting views
Build and evolve the division’s data platform using Databricks or equivalent 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, including lineage, traceability, consistency, and controlled access to data assets
Identify and resolve data issues across upstream and downstream systems, working with source-system owners where needed
Connect data across engineering and operational toolchains, including requirements, design, build, test, deployment, support, and product performance
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
Partner with cross-functional 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
требования
Experience in senior data engineering or closely related roles
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
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 the ability to gather requirements and translate them into practical technical solutions
Будет плюсом: 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
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