Senior Data Engineer - BI & KPIs

About Camlin

Camlin is a global technology leader that operates with the vision of bringing revolutionary products to life for a wide range of industries, including power and rail, and also has interests in a number of R&D projects in a variety of scientific sectors.

At Camlin we believe in high quality engineering and design, allowing us to develop market leading products and services. In short, we love creating value for our customers by solving difficult problems. As of now, Camlin operates in over 20 countries worldwide.

We are seeking a Senior Data Engineer to build the trusted data foundations that enable measurement, insight, and continuous improvement across the Technology Division—spanning SaaS platforms, internal systems, and mechatronics-based products (electromechanical, hardware, embedded software). 

The primary objective of this role is to design, build, and maintain reliable data pipelines, integrations, and analytics-ready models that bring together fragmented operational, engineering, and product data into a trusted foundation for KPI implementation and executive reporting. This role ensures that performance data is accessible, consistent, scalable, and fit for use across the organization. 

About the role

This role sits at the intersection of Data Engineering, Data Platform Delivery, and Analytics Enablement. You will work across business and engineering systems to connect data from multiple sources, establish robust ingestion and transformation pipelines, and create well-structured datasets that support KPI frameworks, business intelligence, and deeper analysis.

Key Responsibilities 

1) Data Platform, Pipelines & Integration 
- 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 robust ETL/ELT processes using SQL, Python, Databricks, and related technologies. 

2) Data Modelling & KPI Enablement 
- Create and maintain analytics-ready data models that support trusted, reusable reporting across the Technology Division. 
- Enable KPI implementation by structuring data so that measures are consistently defined, traceable to source systems, and automatically calculated where possible. 
- Partner with the Technology Performance Analyst and business stakeholders to translate metric definitions into scalable technical models while ensuring semantic consistency across domains so that the same metric means the same thing across teams, products, and reporting views. 

3) Lakehouse / Data Lakehouse Engineering 
- Build and evolve the division’s data platform capabilities using Databricks or equivalent modern data platform technologies. 
- Design data structures and transformation layers that support reporting, self-service analytics, and future advanced analytics use cases. 

4) Data Quality, Governance & Reliability 
- Implement data quality controls, validation checks, and reconciliation processes to improve trust in data used for decision-making. 
- Support data governance practices 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. 

5) Engineering Ecosystem & Toolchain Integration 
- Work across engineering and operational toolchains to connect data spanning requirements, design, build, test, deployment, support, and product performance. 
- Integrate data from sources such as 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.

6) Stakeholder Collaboration & Analytics Enablement 
- Partner with cross-functional teams including Software Engineering, Mechatronics Engineering, Product Ownership, RSO Engineering, Data Solutions, Information Security, and Applied Sciences 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 clearly to both technical and non-technical stakeholders. 

Technical Skills (Essential) 
Strong experience with: 
- SQL for querying, transformation, and performance optimization 
- Python for data engineering, automation, and integration 
- Databricks (or equivalent modern data platform) 
- Building and maintaining ETL/ELT pipelines 
- Data modelling for analytics and reporting 
- Version control and disciplined development practices 
 

Data Engineering Expertise (Essential) 
Experience in senior data engineering or closely related roles. 
Strong understanding of: 
- Data integration across multiple source systems 
- Designing trusted data foundations for analytics and KPI reporting 
- Data quality, lineage, governance, and reliability practices 
- Working with complex, multi-source datasets in operational and engineering environments 

Collaboration & Communication (Essential) 
- Strong stakeholder engagement skills with the ability to gather requirements and translate them into practical technical solutions. 
 

Desirable Skills 
- Familiarity with mechatronics environments (electromechanical systems, hardware, embedded software). 
- 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. 

Our Values 
- We work together 
- We believe in people 
- We won’t accept the ‘way it has always been done
- We listen to learn 
- We’re trying to do the right thing