Sepehr Mohammadi Email & Phone Number

Stockholm, Stockholm County, Sweden

Stockholm, Stockholm County, Sweden

• Technical Lead of the Data Platform architecture responsible for Designing and implementing a scalable, robust AWS-based data platform• Standardized and streamlined stack deployment and resource management across projects by implementing a consistent framework using Infrastructure as Code (IaC) with AWS CloudFormation and Serverless Application Model (SAM)• Engineered and maintained CI/CD pipelines using GitHub Actions for automated code integration and deployment.• Designed and implemented an event-driven architecture and orchestrated ETL pipelines utilizing AWS EventBridge and Step Functions, managing workflows on AWS Batch, Lambda, Glue, Athena, and Redshift resources.• Developed a secure, compliant user management framework for Redshift, streamlining access control and data governance. • Crafted comprehensive 'Ways of Working' guidelines for the team, focusing on collaboration, efficiency, and best practices in navigating a modern data stack.• Engineered an in-house solution for dbt deployment and orchestration.

Uppsala County, Sweden

Collaborate in shaping the program structure to meet the diverse needs of students pursuing tracks in the intersection of Computer Science and Industrial Engineering and Management.

Uppsala, Uppsala County, Sweden

• Led a team of data engineers and scientists, aligning project goals with values and industry best practices.• Communicated effectively with C-level executives to align visions and plan strategic roadmaps.• Developed a self-service data product, reducing data ingestion and model experimentation time by 50%.

• Trading in financial markets using the Deep Reinforcement Learning model to better adapt to the financial market's large-scale data and then directly output actions through brain processing (simulated with Deep Neural Networks) without external supervision. • Utilized AWS Glue to perform data ETL processes, transforming raw financial data into labeled format prepared for deep learning. • Reduced costs by using Snowflake's time-series data handling capabilities. • Designed Event-Driven Architecture for high frequency trading system using AWS Kinesis, Lambda and EventBridge. • Collaborated with ML engineers to deploy deep reinforcement learning models on big data using Apache Spark and TensorFlow.

Built scalable data pipelines with Apache Spark, Apache Kafka, and Hadoop for distributed processing, real-time streaming, and data storage.

Master'S Degree, Computer Science, 4.9/5

Bachelor'S Degree, Engineering/Industrial Management