Daniel A.
Software Engineer @Eliq
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WORK HISTORY
Software Engineer @Eliq
London, GB
Software Engineer (Data) with a strong foundation in data science, machine learning, and backend development. I contribute across the full data and ML lifecycle, building scalable data platforms and production-ready models that deliver energy analytics for tens of millions of smart-metered homes across Europe. I work in a collaborative Agile environment, focusing on reliability, cost-efficiency, and long-term maintainability.Accomplishments: Designed and deployed production machine learning models for time-series forecasting and energy analytics, improving predictive performance and supporting large-scale decision-making. Built and optimised scalable data engineering pipelines processing high-resolution smart meter data, reducing infrastructure costs while supporting significant growth in data volume. Developed robust MLOps workflows for automated training, validation, deployment, and monitoring to ensure safe and repeatable model releases.🧠 Applied advanced analytics and experimentation frameworks to generate actionable insights for utilities and their customers, supporting more informed energy decisions. Speaker at energy-focused industry events and roundtables, including sessions on PV data and AI, contributing to discussions on the role of machine learning in the energy transition.
EDUCATION
Stanwell School
Secondary School
Imperial College London
Master of Engineering - MEng, Mechanical Engineering
ABOUT DANIEL A.
As a Software Engineer at Eliq, I build scalable data products that deliver energy insights and analytics for tens of millions of smart-metered homes across Europe. My work spans the full data and ML lifecycle — from designing data engineering pipelines to developing and deploying machine learning models — with a focus on generating reliable, production-ready insights that help utilities and their customers make more informed energy decisions.I hold an MEng in Mechanical Engineering from Imperial College London, where I developed a strong analytical and first-principles approach to problem solving. I’ve since applied that foundation to time-series forecasting, large-scale energy analytics, and the design of robust ML systems operating in production environments.
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