CRM Data Ingestion Pipeline

Overview

A data ingestion pipeline that captures user data from external sources, maps and transforms it into CRM entities, and persists it into the system database, replacing what used to be a manual, multi-minute process per record.

The pipeline is split across several backend services, each with its own responsibilities and storage. Between the ingestion service and the processing service, a queue is used to decouple the two: ingestion accepts records quickly and hands them off to the queue, while processing workers consume them at their own pace to validate, normalize, and persist. The tradeoff is asynchronous-by-default, clients don't get an immediate persistence guarantee, but bursty traffic stops blocking processing and processing failures don't drop incoming data.

Observability is part of every service: structured logs, metrics, and tracing make failures, retries, queue depth, and worker lag visible in monitoring. Combined with deduplication and idempotency checks, this keeps ingestion stable and the dataset trustworthy even when source layouts change or upstream services misbehave.

My Contribution

• Built the ingestion and processing services and the queue-based handoff between them.
• Designed the data model and mapping rules that convert raw external data into CRM entities.
• Implemented parsing, transformation, and normalization logic with edge-case handling.
• Added deduplication and idempotency checks to keep the dataset clean across retries.
• Wired structured logging, metrics, and tracing for visibility into failures and queue depth.
• Iterated mappings and contracts with product and ops as source layouts changed.