Berlin, Berlin, Germany
Senior Python developer with 7 years in backend services, SaaS platforms, and internal tools. Most of that time has been spent on API design, scheduled jobs, and data services that other teams depend on. A lot of the work has been about keeping contracts clear. I pay attention to idempotency, versioning, and deprecation paths before they turn into support issues. That usually means Django or FastAPI at the edge, PostgreSQL for the data layer, and Celery or asyncio when work needs to run on a schedule or stay off the request path. I've worked on ops tools, e-commerce backends, and fintech APIs. I also spent time on monitoring and route-planning systems, where small changes in query shape or task handling could create noise for everyone downstream. In those cases, the boundary work mattered more than the feature itself. Early on, I worked closer to deployment and data cleanup. That pulled me into backend work because I kept running into the same questions: what breaks, who depends on it, and how do we change it without forcing everyone else to adjust at once. I've led small teams too, with a steady 1:1 cadence and some hiring input. Async Python backends, API-heavy services, and data tooling are where I do my best work. Open to roles focused on backend platforms and API-heavy systems.
Internal Python platform for ops and data work across government and client environments. It supported network monitoring, market data flows, and CRM tasks for pharmaceutical operations. The setup handled 150+ secure network computers and regular system change windows. - Removed routine manual work from client support flows - Tracked backup failures and sent daily reports - Processed market data with Python jobs - Added sensor data handlers for internal devices - Kept change windows stable during coordinated updates - Supported a CRM module for pharma operations - Reviewed code quality with junior and mid-level developers - Containerized a web app with Docker for easier deployment - Monitored internet use for security compliance - Used Django, PostgreSQL, asyncio, AWS, Git, and Pydantic in daily work
Commerce analytics platform for search, reporting, and product data. Internal users relied on it for faster analysis and cleaner operational views across several product areas. - Led Python service work for reporting and data automation flows - Shipped FastAPI endpoints for CRUD actions and internal tools - Added Celery jobs for scheduled processing and delayed tasks - Tuned PostgreSQL queries for large dataset runs and dashboard reads - Used Redis for caching and short-lived task state - Wrote pytest coverage for API logic and data rules - Packed services with Docker for local and release builds - Kept AWS deployment work aligned with release timing - Built Flask and Django modules where older services needed changes to
Internal Python services for data handling and support tasks. The work covered cloud deployments, database fixes, and batch jobs for a small cross-functional team. - Used Django ORM and PostgreSQL for safer data updates - Wrote Docker Compose setups for local service runs - Built boto3 jobs for AWS data processing - Added pytest coverage for backend changes - Kept release code style consistent - Fixed batch jobs that failed on bad input - Supported Azure-based environments for internal use - Reduced routine processing time by about 20%
Internal dispatch and route-planning tools for operations teams. The work covered web features, Telegram bots, and security fixes for staff handling daily work. - Built Telegram bots with aiogram for internal workflows - Improved route planning with Google OR-Tools - Fixed security issues in Python and React code - Kept 4 developers aligned during prototype handoff - Added state handling for long-running tasks - Supported rollout work across internal tools - Cleaned up code paths before production release