About Camlin Group:

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 today, the Camlin operation spans over 20 countries across the globe.

As a Data Engineer at Camlin, you will be instrumental in shaping the future of energy through data innovation. Collaborating closely with our dynamic team of data engineers, machine learning experts, and data scientists, you'll play a pivotal role in identifying the data-related needs of our organization and contributing to the execution of strategic plans to fulfil them.

Your responsibilities will span a diverse range of tasks, from architecting software solutions to designing data models and implementing cutting-edge data science and machine learning algorithms. You'll be dedicated to enhancing our tools and applications by proactively addressing bugs, performing code refactoring, and ensuring top-notch quality.

We are looking for candidates with diverse levels of experience, spanning from mid-level to senior-level positions.

We are looking for a Senior Data Engineer to join our team in a highly technical role, with a strong focus on building real data and AI products that deliver measurable outcomes.

This is not a traditional “pipeline-only” data engineering role. You will work across the data, ML/AI, and product stack, helping turn uncertainty into clear plans, reduce technical and product risk early, and drive predictable delivery in complex environments.

You will start as a hands-on contributor with strong leadership influence and, depending on fit and impact, can grow into a technical leadership role very quickly.

What you will do:

•  Drive the design and development of data and AI-enabled products, from early exploration through validated, production-ready models
• Help turn ambiguous problem statements into clear delivery plans, making assumptions, risks, and trade-offs explicit
• Lead risk reduction activities (e.g. prototypes, spikes, technical validation) early in the lifecycle
• Contribute to and influence product definition, ensuring solutions are technically feasible and scalable
• Define and refine clear success metrics for data and AI initiatives, aligned with product and business goals
• Facilitate iterative, predictable delivery under uncertainty
•  Communicate effectively with management and non-technical stakeholders, including escalating when needed.
• Actively contribute to improving ways of working through a constructive, open, and disciplined approach

What we are looking for:

Must have

• Strong experience as a data science, data engineer or similar role, working across the broader data stack
• Proven ability to operate effectively in ambiguous problem spaces
• Experience building data products, not just pipelines
• Solid understanding of ML/AI concepts and how models are integrated into real systems
• Ability to reason about trade-offs (accuracy vs cost, latency, reliability, complexity)
• Strong communication skills and comfort interacting with management and product stakeholders
• A results-oriented mindset: you care about outcomes, not just implementations
• Willingness to take ownership, ask hard questions, and drive clarity

Nice to have

• Experience working with AI/ML systems in production
• Familiarity with modern data platforms (e.g. cloud data warehouses, lakehouses, streaming systems)
• Experience influencing product direction without being a formal product manager
• Previous experience stepping into a technical leadership or tech lead role

What this role is not:

To be very clear, this role is not a good fit if you are:

• Primarily a pipeline or ETL-only engineer
• Most comfortable working from fully specified requirements
• Focused mainly on academic or research-oriented ML
• Uncomfortable discussing trade-offs, risks, or constraints with management

What success looks like:

In your first 6–12 months, success in this role looks like:

• Reduced uncertainty and risk on key data and AI initiatives
• Clear delivery plans where ambiguity previously existed
• Data and AI products with well-defined, measurable success metrics
• More predictable iteration and delivery across the team
• Emerging technical leadership and increased trust from engineers, product, and management

Benefits:

• 25 Days of Annual Leave
• FitPass membership
• Private Health Insurance
• Internal Reward & Recognition Tool Kudos