Data Engineer, 2018 - Present at Google
- Developed data pipelines to ingest and process terabytes of streaming data daily.
- Collaborated with cross-functional teams to optimize data storage and retrieval processes.
- Implemented machine learning models for predictive analytics on large-scale datasets.

Data Analyst Intern, 2017 - 2018 at Microsoft
- Assisted in cleaning and organizing raw data from various sources for analysis.
- Conducted exploratory data analysis to identify trends and patterns within the dataset.
- Created visualizations and dashboards to present findings to stakeholders.
- Proficient in designing and implementing scalable data pipelines using technologies such as Apache Spark, Kafka, and Hadoop to process large volumes of data efficiently.
- Skilled in optimizing database performance and ensuring data quality by developing robust ETL processes and data validation frameworks.
- Experienced in building and maintaining data infrastructure on cloud platforms like AWS or GCP, leveraging services like S3, Redshift, and BigQuery.
- Strong background in programming languages such as Python, SQL, and Scala for data manipulation and analysis tasks.
The individual began publishing academic research in the field of data engineering in 2017, contributing to the advancement of knowledge and best practices in the industry. Their publications likely focus on topics such as data processing, ETL (extract, transform, load) processes, and scalable data architectures.
- Bachelor of Science in Computer Science, Lund University, 2014
- Master of Science in Data Engineering, Stockholm University, 2016
- Thesis: "Optimizing Big Data Processing Techniques for Real-time Analytics"
- Specialization in Machine Learning and Data Visualization
- Internship at a leading tech company focusing on data engineering solutions
- Fluent in Swedish and English
- Proficient in designing and implementing scalable data pipelines using technologies such as Apache Spark, Kafka, and Hadoop to process large volumes of data efficiently.
- Skilled in optimizing database performance and ensuring data quality by developing robust ETL processes and data validation frameworks.
- Experienced in building and maintaining data infrastructure on cloud platforms like AWS or GCP, leveraging services like S3, Redshift, and BigQuery.
- Strong background in programming languages such as Python, SQL, and Scala for data manipulation and analysis tasks.

- Bachelor of Science in Computer Science, Lund University, 2014
- Master of Science in Data Engineering, Stockholm University, 2016
- Thesis: "Optimizing Big Data Processing Techniques for Real-time Analytics"
- Specialization in Machine Learning and Data Visualization
- Internship at a leading tech company focusing on data engineering solutions
- Fluent in Swedish and English



The individual began publishing academic research in the field of data engineering in 2017, contributing to the advancement of knowledge and best practices in the industry. Their publications likely focus on topics such as data processing, ETL (extract, transform, load) processes, and scalable data architectures.

Data Engineer, 2018 - Present at Google
- Developed data pipelines to ingest and process terabytes of streaming data daily.
- Collaborated with cross-functional teams to optimize data storage and retrieval processes.
- Implemented machine learning models for predictive analytics on large-scale datasets.

Data Analyst Intern, 2017 - 2018 at Microsoft
- Assisted in cleaning and organizing raw data from various sources for analysis.
- Conducted exploratory data analysis to identify trends and patterns within the dataset.
- Created visualizations and dashboards to present findings to stakeholders.

