Senior Data Engineer

03 ago - Parma
Camlin

What you'll work on:
You’ll help build AI and data products used in energy environments, including:
- Predictive maintenance systems for critical infrastructure
- Anomaly detection on sensor data
- Asset monitoring and operational intelligence platforms
- Energy forecasting and time-series modelling systems
- Production ML systems operating on large-scale multimodal datasets

The work involves difficult real-world constraints:
- Noisy and incomplete sensor data
- Sparse labels and uncertain signals
- Large-scale time-series datasets
- Scaling AI systems into real world solutions
- Translating unclear requirements into practical delivery plans

What you'll do:
- Design and build AI and data systems from early concept through model creation
- Work closely 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 early
- 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
- Help drive measurable outcomes with clear KPIs and visible business impact
- Improve engineering quality, maintainability, and operational reliability across the stack

Our current tech stack includes:
- Python
- Apache Spark
- Databricks
- MLflow
- Modern cloud-based data platforms
- Large-scale industrial and time-series datasets

What we are looking for:
- Experience building real-world AI, ML, or data products
- Experience working across data engineering and ML systems, not just isolated pipelines
- Comfortable 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
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