ETL Management System
We contributed to the development of a scalable integration platform with a multi-tenant architecture, purpose-built to streamline data synchronization and ETL workflows across a variety of client environments. Focused on automation, flexibility, and security, the platform enables seamless movement of data between systems using a modular, code-based approach.
Technologies
Python
Project Challenges
- Supporting multi-tenant deployments while maintaining full data isolation and scalability.
- Building custom ETL pipelines that adapt to varying source/target systems.
- Managing infrastructure-level operations, including per-tenant orchestration on AWS.
- Ensuring reliable, automated data movement without a traditional user interface.
- Balancing customization and standardization across diverse client use cases.
The Process
Fast, Structured Onboarding
We began with joint workshops and onboarding sessions to align the teams on objectives, timelines, and workflows laying the foundation for smooth and seamless collaboration from the very start.
Agile Execution for Rapid Progress
By applying Agile methodology, we broke the migration into manageable workstreams, enabling our team to operate independently while reducing the need for continuous coordination.
24-Hour Workflow for Maximum Productivity
We transformed time zone differences into an advantage. The Polish team completed tasks and submitted approval requests during their workday, which the San Francisco team reviewed and responded to as their day began. This seamless handoff created a continuous feedback loop, accelerating development and virtually eliminating downtime.
Independent Work Model for Faster Delivery
The project was organized into self-contained components, enabling the team to move quickly and independently, avoiding bottlenecks and significantly shortening the migration timeline.
Solutions
- Developed and maintained custom Python ETL scripts using the Singer SDK, utilizing reusable taps and targets for structured data pipelines.
- Automated the deployment of isolated tenant environments in AWS, enabling secure, scalable processing.
- Implemented monitoring and logging to track ETL performance, failures, and data integrity.
- Focused on infrastructure-centric automation instead of frontend/backend application flows.
- Streamlined cross-system data integration through standardized patterns and reusable components.
Results and Impact
- Delivered a highly scalable, multi-tenant ETL platform that ensures robust and isolated data workflows.
- Enabled faster onboarding and deployment of new clients through automated instance provisioning.
- Improved data accuracy and processing reliability through well-maintained custom ETL pipelines.
- Provided a secure, infrastructure-driven alternative to traditional integration tools.
- Contributed to a system capable of handling complex data transformations across enterprise environments.
