- I enjoy skiing in the winter and hitting the slopes for some adrenaline-filled fun.
- Photography is another passion of mine, capturing beautiful moments and scenes through my lens.
- Cooking is a therapeutic activity for me, experimenting with new recipes and flavors in the kitchen.
- I love hiking in nature, exploring different trails and immersing myself in the great outdoors.
- Playing the guitar is a creative outlet for me, strumming away to my favorite tunes in my free time.
Nicolas Perez can be reached at nperez@stregisnyc.com for any inquiries. His address is 2 East 55th Street, New York City, USA. You can contact him directly at +1-212-753-4500.
Data Engineer, 2020 - Present at Google
- Develop and maintain data pipelines to process large volumes of real-time data.
- Collaborate with cross-functional teams to design scalable solutions for data storage and retrieval.
- Implement machine learning models to optimize data processing efficiency.
- Conduct performance tuning and troubleshooting to ensure the reliability of data infrastructure.

Intern Data Engineer, Summer 2019 at Microsoft
- Assisted in building ETL processes to extract, transform, and load data into a centralized repository.
- Participated in data quality assessments and implemented data cleansing techniques.
- Contributed to the development of data visualization dashboards for monitoring system performance.
- Supported senior engineers in optimizing database queries for faster data retrieval.
- Proficient in designing and implementing scalable data pipelines using tools such as Apache Spark and Kafka, with a strong focus on optimizing performance and reliability.
- Skilled in building and maintaining data infrastructure on cloud platforms like AWS, leveraging services like S3, Redshift, and EMR to enable efficient data processing and analysis.
- 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
- Master of Science in Data Engineering, Aalto University, 2020
- Bachelor of Science in Computer Science, Helsinki University, 2017
- Specialization in Big Data Technologies and Analytics
- Thesis: "Optimizing Real-time Data Processing Pipelines using Apache Spark"
- Relevant coursework: Machine Learning, Data Warehousing, Cloud Computing
- Internship at a leading Finnish tech company specializing in data analytics, 2019
Nicolas Perez can be reached at nperez@stregisnyc.com for any inquiries. His address is 2 East 55th Street, New York City, USA. You can contact him directly at +1-212-753-4500.

- I enjoy skiing in the winter and hitting the slopes for some adrenaline-filled fun.
- Photography is another passion of mine, capturing beautiful moments and scenes through my lens.
- Cooking is a therapeutic activity for me, experimenting with new recipes and flavors in the kitchen.
- I love hiking in nature, exploring different trails and immersing myself in the great outdoors.
- Playing the guitar is a creative outlet for me, strumming away to my favorite tunes in my free time.

- Master of Science in Data Engineering, Aalto University, 2020
- Bachelor of Science in Computer Science, Helsinki University, 2017
- Specialization in Big Data Technologies and Analytics
- Thesis: "Optimizing Real-time Data Processing Pipelines using Apache Spark"
- Relevant coursework: Machine Learning, Data Warehousing, Cloud Computing
- Internship at a leading Finnish tech company specializing in data analytics, 2019

- Proficient in designing and implementing scalable data pipelines using tools such as Apache Spark and Kafka, with a strong focus on optimizing performance and reliability.
- Skilled in building and maintaining data infrastructure on cloud platforms like AWS, leveraging services like S3, Redshift, and EMR to enable efficient data processing and analysis.

Data Engineer, 2020 - Present at Google
- Develop and maintain data pipelines to process large volumes of real-time data.
- Collaborate with cross-functional teams to design scalable solutions for data storage and retrieval.
- Implement machine learning models to optimize data processing efficiency.
- Conduct performance tuning and troubleshooting to ensure the reliability of data infrastructure.

Intern Data Engineer, Summer 2019 at Microsoft
- Assisted in building ETL processes to extract, transform, and load data into a centralized repository.
- Participated in data quality assessments and implemented data cleansing techniques.
- Contributed to the development of data visualization dashboards for monitoring system performance.
- Supported senior engineers in optimizing database queries for faster data retrieval.

- 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