data engineer in energy technology

Camlin develops industrial technology and AI-driven systems for the energy sector across more than 20 countries. Its products address critical infrastructure, industrial sensor networks, predictive maintenance, anomaly detection, asset monitoring, operational intelligence, and energy forecasting.

• Design and build AI and data systems from early concept through model creation;
• Work with data scientists, product managers, and domain experts to shape technical solutions;
• Turn ambiguous business and operational problems into structured engineering plans;
• Prototype and validate ideas quickly to reduce technical and product risk;
• Build scalable data and ML workflows using modern cloud-native tooling;
• Influence technical direction, architecture decisions, and product roadmap discussions;
• Evaluate trade-offs between model accuracy, reliability, complexity, latency, and operational cost;
• Drive measurable outcomes with clear KPIs and visible business impact;
• Improve engineering quality, maintainability, and operational reliability across the stack;
• Shape technical plans, mentor engineers, and take ownership of larger AI and data initiatives over time.

• Experience building real-world AI, ML, or data products;
• Experience working across data engineering and ML systems, not just isolated pipelines;
• Comfort operating in environments with uncertainty and evolving requirements;
• Pragmatic engineering mindset focused on outcomes and business impact;
• Ability to make and communicate trade-offs clearly;
• Systems thinking that connects architecture, product goals, and operational constraints;
• Strong communication skills with technical and non-technical stakeholders;
• Collaborative approach with a willingness to challenge assumptions constructively;
• Pipeline-only or ETL-focused engineers are not a good fit;
• Candidates who prefer fully specified requirements before starting work are not a good fit;
• Candidates seeking narrowly scoped responsibilities without product or technical ownership are not a good fit.

• Hybrid schedule: 3 days on-site and 2 days working from home;
• 25 Days of annual leave;
• FitPass membership;
• Private health insurance;
• Internal reward and recognition tool Kudos.