Open to opportunities

Charles User

@charlesuser16

I build reliable cloud-native data platforms and pipelines that improve speed, stability, and infrastructure efficiency.

Sweden

Message

At Viaplay Group, I lead data platform infrastructure for a company-wide migration, scaling Infrastructure-as-Code-managed cloud infrastructure by 50% and reducing infrastructure development time by 66% through agentic infrastructure summarization.

I've improved pipeline stability by 75%, reduced processing times by 50–85%, and built Databricks, Spark, and Airflow pipelines. Earlier, I contributed to Auto-sklearn at ABB while developing AutoML forecasting methods for energy data.

Experience

Work history, roles, and key accomplishments

Current

Senior Data Engineer

Current

Viaplay Group

Sep 2025 - Present (1 year)

Led and designed data platform infrastructure for a company-wide migration, scaling cloud infrastructure by 50% and introducing agentic infrastructure summarization that cut development time by 66%. Streamlined data ingestion post-acquisition and leveraged Databricks to reduce ingestion overhead by 50%.

Data Engineering Cloud Infrastructure Infrastructure as Code Databricks AWS Terraform AWS CDK Data Pipelines Data Ingestion

Data Engineer

Viaplay Group

Aug 2022 - Aug 2025 (3 years)

Advised and supported the data warehouse migration project, boosting analytics engineers' modeling workflow speed by 75% with a CI/CD framework and reducing manual maintenance by 50% via a serverless Databricks warehouse. Coached junior engineers and automated cluster reboots, cutting communication overhead by over 90%.

Data Engineering Data Warehouse Databricks CI CD GitHub Actions Data Modeling Cloud Applications Automation Spark Airflow SQL Python Scala

Tech Trainee – Rotational Program

Viaplay Group

Sep 2021 - Aug 2022 (11 months)

Developed back-end services in TypeScript and JavaScript, modularized reusable functions for data cleansing, feature engineering, and visualization in Databricks, and designed ETL pipelines in Spark orchestrated with Airflow.

TypeScript JavaScript Databricks ETL Spark Airflow Data Cleaning Feature Engineering Data Visualization

Full-Stack Developer

Pluss

Feb 2021 - Jun 2021 (4 months)

Collaborated with the product manager to validate use cases across front-end and back-end of a web application, and redesigned problematic data modeling for improved scalability and maintainability.

Fullstack Development Web Applications Data Modeling Frontend Backend Product Management

Master Thesis Researcher – AutoML for Energy Forecasting

ABB

Sep 2019 - Sep 2020 (1 year)

Tailored AutoML pipelines for multi-step-ahead energy data prediction, saving up to 70% of time versus manual ML model tuning, and devised an algorithm for measuring uncertainty in ensemble tree-based models. Contributed to the open-source AutoML package Auto-sklearn with two merged pull requests.

AutoML Machine Learning Energy Forecasting Python Auto Sklearn Ensemble Models Uncertainty Quantification Data Analysis

Education

Degrees, certifications, and relevant coursework

KU Leuven

Master of Science, Energy and Electrical Engineering

2018 - 2020

Grade: 16/20

Activities and societies: Elective AI/ML coursework; Datathon 2020 Best Visualization Team.

Pursued a double Master of Science in Energy and Electrical Engineering, with a master thesis on AutoML for energy forecasting. Achieved a grade of 16/20 for the thesis.

KTH Royal Institute of Technology

Master of Science, Energy and Electrical Engineering

2018 - 2020

Grade: 16/20

Activities and societies: Elective AI/ML coursework; Datathon 2020 Best Visualization Team.

Pursued a double Master of Science in Energy and Electrical Engineering, with a master thesis on AutoML for energy forecasting. Achieved a grade of 16/20 for the thesis.

National Cheng Kung University

Bachelor of Science, Electrical Engineering

2012 - 2017

Activities and societies: Outstanding Student 2014; computer science coursework.

Earned a Bachelor of Science in Electrical Engineering, with coursework in computer science and related fields. Recognized as an Outstanding Student in 2014, ranking in the top 5%.