potiuk/airflow
Oct 2025 – Jan 2026
2 Months active
Languages Used
Python
Technical Skills
AWSAirflowPythonSSMSystem DesignTesting
Kamen
PROFILE
Kamen
Kiril Sharlandjiev contributed to the potiuk/airflow repository by developing advanced AWS SSM integration features for Airflow, focusing on reliability and observability in distributed workflows. He engineered the SsmGetCommandInvocationOperator to retrieve detailed execution metadata and outputs from SSM commands across multiple instances, addressing multi-instance orchestration challenges. Using Python and leveraging Airflow’s extensibility, he improved AWS connection parameter handling and introduced exit-code-based routing for SsmRunCommandOperator, enabling precise error detection and workflow branching. His work included comprehensive unit and system tests, robust documentation, and enhanced logging, reflecting a deep, methodical approach to backend development and DevOps automation in cloud environments.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
2
Lines of code
1,430
Activity Months2
Your Network
284 peopleWork History
January 2026
2 Commits • 1 Features
Jan 1, 2026January 2026: Airflow work on potiuk/airflow focused on strengthening SSM command execution resilience and routing capability. Delivered exit-code-based routing enhancements and deferrable-mode robustness for SsmRunCommandOperator, along with comprehensive tests and documentation. Implemented centralized failure detection logic to distinguish AWS-level vs command-level failures, enabling precise error handling and routing. Expanded test coverage for various exit-code routing scenarios and updated SSM documentation for migration and best practices.
2 Commits • 1 Features
Jan 1, 2026January 2026: Airflow work on potiuk/airflow focused on strengthening SSM command execution resilience and routing capability. Delivered exit-code-based routing enhancements and deferrable-mode robustness for SsmRunCommandOperator, along with comprehensive tests and documentation. Implemented centralized failure detection logic to distinguish AWS-level vs command-level failures, enabling precise error handling and routing. Expanded test coverage for various exit-code routing scenarios and updated SSM documentation for migration and best practices.
January 2026
October 2025
1 Commits • 1 Features
Oct 1, 2025Month: 2025-10 — Key SSM integration improvements for Airflow (potiuk/airflow) focusing on reliability, observability, and multi-instance execution. Delivered SsmGetCommandInvocationOperator to retrieve stdout/stderr and execution metadata for SSM commands across single or all instances; fixed deferrable command issues and enhanced AWS connection parameter passing; included comprehensive unit tests, documentation updates, and system test integration. Resolved NoRegionError in deferrable flows and extended SsmRunCommandTrigger/SsmRunCommandOperator to support region_name, verify, and botocore_config, ensuring AWS connection parameters are properly propagated. These changes reduce manual debugging, improve automation reliability, and enable more scalable SSM-based workflows.
October 2025
1 Commits • 1 Features
Oct 1, 2025Month: 2025-10 — Key SSM integration improvements for Airflow (potiuk/airflow) focusing on reliability, observability, and multi-instance execution. Delivered SsmGetCommandInvocationOperator to retrieve stdout/stderr and execution metadata for SSM commands across single or all instances; fixed deferrable command issues and enhanced AWS connection parameter passing; included comprehensive unit tests, documentation updates, and system test integration. Resolved NoRegionError in deferrable flows and extended SsmRunCommandTrigger/SsmRunCommandOperator to support region_name, verify, and botocore_config, ensuring AWS connection parameters are properly propagated. These changes reduce manual debugging, improve automation reliability, and enable more scalable SSM-based workflows.
Activity
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Quality Metrics
Correctness100.0%
Maintainability86.6%
Architecture93.4%
Performance80.0%
AI Usage26.6%
Skills & Technologies
Programming Languages
Python
Technical Skills
AWSAirflowDevOpsPythonSSMSystem DesignTestingbackend developmenttesting
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