Tech Lead – Advanced Analytics

The Advanced Analytics team is part of the Power & Gas Trading IT department and develops software products that enable advanced quantitative analytics across European and Worldwide energy markets. Our mission is to build scalable, production-grade applications supporting Traders, Quantitative Analysts and Market Analysts in forecasting, optimization, simulation and data analysis. Working closely with quantitative teams, we transform analytical and machine learning models into robust software products capable of operating reliably in production while remaining flexible for future evolution.

As a Tech

Lead , you will provide technical leadership across the Advanced Analytics product landscape. You will combine hands-on software engineering with architectural thinking, guiding technical decisions, promoting engineering excellence, and partnering with quantitative stakeholders to deliver high-quality analytics platforms. This is not a people management role . Success in this position comes from technical expertise, influence, mentoring and the ability to drive engineering best practices across multiple initiatives.

As a Tech Lead, your main responsibilities will be to:

• Lead the technical design and implementation of software products supporting forecasting, optimization, simulation and advanced analytics use cases.
• Drive software architecture and technical decisions in collaboration with Solution Architects, ensuring consistency, scalability, security and maintainability across the Advanced Analytics landscape.
• Partner closely with Quantitative Analysts, Traders and business stakeholders to understand, challenge and refine functional and technical requirements.
• Translate quantitative research into reliable, production-ready software solutions while ensuring long-term maintainability and extensibility.
• Design cloud-native applications following modern engineering principles and best practices for reliability, observability, security and cost optimization.
• Lead the industrialization of analytical and machine learning models, ensuring reproducibility, scalability, monitoring and operational excellence.
• Contribute to software development, code reviews, technical investigations and complex implementations.
• Promote engineering excellence through coding standards, automated testing, CI/CD, documentation and continuous improvement.
• Mentor and support engineers through technical coaching, architectural guidance and knowledge sharing, without direct line management responsibilities.
• Support production systems, troubleshoot complex technical issues and drive continuous improvement of existing applications. In addition, the Tech Lead will be expected to:
• Own technical decisions from solution design through production delivery.
• Establish and promote engineering standards across the Advanced Analytics team.
• Balance short-term delivery objectives with long-term architectural sustainability.
• Make sound technical decisions in environments with evolving requirements and incomplete information.
• Identify technical risks early and propose pragmatic, scalable solutions.
• Drive adoption of modern software engineering practices and emerging technologies where they provide tangible business value.
• Build credibility with both software engineers and quantitative experts by combining strong engineering capabilities with an understanding of analytical modelling challenges.
• Influence technical direction through collaboration, expertise and leadership rather than formal authority.

Education

• Master's degree in Computer Science, Software Engineering, Mathematics, Physics, Engineering or related technical discipline. Experience
• Minimum 6 years of professional experience in software development, data science or related fields, including experience acting as a technical lead or senior technical contributor on complex software products.
• Experience deploying, operating and maintaining analytical or machine learning solutions in production environments.
• Experience working in Agile product teams within complex enterprise environments.
• Experience working in the energy trading industry, particularly in power markets, would be considered an advantage. Technical

Expert-level proficiency in Python and experience building production-grade backend applications.

• Strong experience designing cloud-native applications and distributed systems on AWS.
• Strong understanding of software architecture, design patterns, API design and system integration.
• Experience designing RESTful APIs and event-driven architectures.
• Experience with containerization technologies such as Docker and orchestration platforms including Kubernetes and ECS.
• Experience implementing CI/CD pipelines and Infrastructure as Code.
• Solid understanding of relational databases, SQL, data modelling and modern data platform architectures.
• Hands-on experience with Databricks, MLflow or comparable modern data and MLOps platforms.
• Strong knowledge of software engineering best practices, including automated testing, code reviews, observability and production support.
• Familiarity with Lakehouse architectures and modern Data Engineering practices would be an asset.
• Experience developing modern frontend applications and data visualization tools using React, or similar frameworks, would be considered a plus.

Excellent communication and stakeholder management skills, with the ability to interact effectively with both technical and quantitative audiences.

• Strong analytical and problem-solving capabilities.
• Demonstrated ability to mentor engineers and provide technical leadership through influence.
• Ability to work effectively in a fast-paced, collaborative and international environment.
• Strong commitment to engineering excellence and continuous improvement.

Preferred qualifications include:

• PhD in Computer Science, Mathematics, Physics, Operations Research, Machine Learning, Artificial Intelligence or another quantitative discipline.
• Understanding of European energy systems, electricity markets and quantitative trading workflows.
• Experience with forecasting, optimization, time-series analysis or other quantitative modelling techniques, together with an understanding of the challenges involved in bringing analytical models into production.
• Experience building data-intensive or AI-enabled platforms supporting quantitative research.
• Knowledge of MLOps best practices, including model lifecycle management, model versioning, reproducibility, monitoring and governance.
• Experience leveraging AI-assisted software development tools (e.g. GitHub Copilot, Claude Code, Codex or equivalent) to improve engineering productivity while maintaining high standards of software quality.

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