Backend systems and third-party integrations

Enterprise Integration Platform

Reusable integration infrastructure for retail clients that moved the company beyond one-off integration builds and into shared connector patterns, operational tooling, and production data synchronization.

Business Problem

Retail clients needed reliable movement of catalog, order, customer, inventory, and accounting data across systems that were not designed to work together. Earlier integration work often required repeated implementation effort, client-specific logic, and manual troubleshooting when third-party APIs failed or changed behavior.

Systems and Domains

ShopifyTeamwork CommerceVirid / MarketagilityQuickBooks Online

Role: Co-architected the platform, designed reusable connector patterns, and owned production integration work across multiple third-party systems.
Team Delivery: The engineering team delivered a multi-client integration platform that supported reusable connectors, shared service patterns, and production workflows across retail systems.

Owned

/Connector design and API integration implementation
/Data mapping, validation, and synchronization workflows
/Retry, recovery, and partial-failure handling patterns
/Production troubleshooting and operational support for live integrations

Sanitized Architecture View

This diagram is recreated at a high level to explain system shape without exposing proprietary implementation details.

Source Systems

Shopify, Teamwork, Virid, QBO

Connector Layer

Auth, mapping, validation

Processing Layer

Queues, retries, recovery

Platform Services

Shared APIs, storage, monitoring

Destination Systems

Retail operations and reporting

Architecture

The platform separated system-specific connector logic from shared processing concerns. Connectors handled authentication, API-specific requests, field mapping, and normalization. Shared services handled persistence, scheduled work, retry behavior, and operational visibility. This made new integrations faster to deliver while keeping failure handling and support patterns consistent.

Data Flow

Data moved between external systems and internal services through connector-specific adapters, validation and normalization steps, processing queues, and destination-specific sync workflows. The design accounted for imperfect third-party APIs, inconsistent payloads, pagination limits, and long-running batch updates.

Failure Handling

The platform needed to recover from rate limits, partial API outages, malformed records, duplicate payloads, and interrupted syncs. Retry and recovery behavior was designed so individual record failures did not necessarily block entire synchronization jobs. Production support patterns emphasized traceability and clear handoff from failed operation to resolution.

Operational Concerns

The work was shaped by real production support needs: identifying where a sync failed, determining whether data was safe to replay, handling client-specific exceptions without fragmenting the platform, and keeping revenue-critical workflows moving while third-party systems behaved unpredictably.

Outcome

The platform successfully reduced duplicated implementation work by centralizing repeated connector concerns into reusable patterns. It also gave the team a stronger foundation for building, supporting, and troubleshooting production integrations across multiple retail clients.