Work experience
Key roles, responsibilities, and results.
I develop the backend of Easy Sellers, an analytics platform for marketplace sellers: product architecture, integrations, background processing, observability, and release stability.
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Design backend architecture: modules, contexts, API contracts, and team-level technical standards.
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Define non-functional requirements around stability, performance, logging, metrics, and tracing.
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Own critical areas: complex integrations, incidents, performance hot spots, and stabilization work.
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Break down epics and features, assess risks, and coordinate delivery with QA, DevOps, and analytics.
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Run code reviews, protect architectural boundaries, and help the team avoid accumulating technical debt.
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Take part in hiring, onboarding, and developing backend engineers.
Worked on a high-load marketplace and PIM system: maintaining a busy backend, delivering data through brokers, improving performance, and bringing an MVP into production.
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Maintained a high-load backend: fixed defects, investigated degradations, handled incidents, and worked with monitoring.
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Contributed to Kafka adoption for product data updates with retries, failure handling, and resilient processing.
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Optimized heavy database queries, reduced API latency, and removed bottlenecks in business flows.
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Helped design a PIM system from scratch: data model, product lifecycle, offers, and integration boundaries.
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Helped deliver the MVP on schedule: feature breakdown, implementation, integration stabilization, and production readiness.
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Designed RabbitMQ events and messages with error handling, retries, idempotency, and service contracts.
Developed a PIM system for Golden Apple: data model, import and export flows, legacy storage and storefront integrations, and MVP stabilization.
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Contributed to MVP development and later integrations: data requirements, exchange flows, and stable system interaction.
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Designed and implemented the PIM data model: entities, relationships, validation rules, import, and export.
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Integrated the PIM with legacy storage and the storefront: exchange formats, partial updates, and edge cases.
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Investigated data quality issues: empty values, broken records, format mismatches, and incomplete updates.