- I am a Data Engineer who enjoys playing football in my free time.
- I have a passion for cooking and experimenting with new recipes.
- Photography is another hobby of mine, capturing moments and scenes that inspire me.
- I love hiking and exploring the great outdoors, connecting with nature.
- Reading fiction novels is a favorite pastime of mine, transporting me to different worlds.
Data Engineer, 2020 - Present at Amazon
- Design and implement scalable data pipelines to process large volumes of real-time data for analytics purposes.
- Collaborate with cross-functional teams to optimize data storage and retrieval processes, improving overall system performance.
- Develop monitoring tools to ensure data quality and integrity across various databases.

Data Engineering Intern, Summer 2019 at Google
- Assisted in building machine learning models to predict user behavior patterns based on historical data.
- Conducted data cleaning and preprocessing tasks to prepare datasets for analysis using Python and SQL.
- Contributed to the development of a data visualization dashboard to track key performance metrics for internal stakeholders.
- Proficient in designing and implementing scalable data pipelines using tools such as Apache Spark and Kafka to process large volumes of data efficiently.
- Skilled in optimizing database performance and ensuring data quality by leveraging SQL queries, indexing strategies, and ETL processes.
- Title: "Optimizing Data Pipelines for Real-Time Analytics"
  Authors: John Smith, Jane Doe
  Publication Year: 2020
  Journal: Big Data Research

- Title: "Scalable Data Processing Techniques for Large-Scale IoT Applications"
  Authors: John Smith, Emily Johnson
  Publication Year: 2021
  Conference: IEEE International Conference on Big Data

- Title: "Automated Data Quality Assessment in ETL Processes"
  Authors: John Smith, Michael Brown
  Publication Year: 2022
  Journal: Data Engineering and Management

- Title: "Efficient Resource Allocation Strategies for Cloud-Based Data Warehouses"
  Authors: John Smith, Sarah Wilson
  Publication Year: 2023
  Conference: ACM SIGMOD International Conference on Management of Data
A bachelor's degree in Computer Science from Stockholm University in 2017 and a master's degree in Data Engineering from KTH Royal Institute of Technology in 2020 were successfully completed.
A bachelor's degree in Computer Science from Stockholm University in 2017 and a master's degree in Data Engineering from KTH Royal Institute of Technology in 2020 were successfully completed.

- Title: "Optimizing Data Pipelines for Real-Time Analytics"
  Authors: John Smith, Jane Doe
  Publication Year: 2020
  Journal: Big Data Research

- Title: "Scalable Data Processing Techniques for Large-Scale IoT Applications"
  Authors: John Smith, Emily Johnson
  Publication Year: 2021
  Conference: IEEE International Conference on Big Data

- Title: "Automated Data Quality Assessment in ETL Processes"
  Authors: John Smith, Michael Brown
  Publication Year: 2022
  Journal: Data Engineering and Management

- Title: "Efficient Resource Allocation Strategies for Cloud-Based Data Warehouses"
  Authors: John Smith, Sarah Wilson
  Publication Year: 2023
  Conference: ACM SIGMOD International Conference on Management of Data

- Proficient in designing and implementing scalable data pipelines using tools such as Apache Spark and Kafka to process large volumes of data efficiently.
- Skilled in optimizing database performance and ensuring data quality by leveraging SQL queries, indexing strategies, and ETL processes.

- I am a Data Engineer who enjoys playing football in my free time.
- I have a passion for cooking and experimenting with new recipes.
- Photography is another hobby of mine, capturing moments and scenes that inspire me.
- I love hiking and exploring the great outdoors, connecting with nature.
- Reading fiction novels is a favorite pastime of mine, transporting me to different worlds.



Data Engineer, 2020 - Present at Amazon
- Design and implement scalable data pipelines to process large volumes of real-time data for analytics purposes.
- Collaborate with cross-functional teams to optimize data storage and retrieval processes, improving overall system performance.
- Develop monitoring tools to ensure data quality and integrity across various databases.

Data Engineering Intern, Summer 2019 at Google
- Assisted in building machine learning models to predict user behavior patterns based on historical data.
- Conducted data cleaning and preprocessing tasks to prepare datasets for analysis using Python and SQL.
- Contributed to the development of a data visualization dashboard to track key performance metrics for internal stakeholders.