The Challenge
The company had reached a point where its existing data infrastructure could no longer keep pace with its growth. Migrating data from third-party platforms was slow and risky, new client onboarding stretched across months, and there was no reliable way to guarantee the quality of data moving through the system. Every new client engagement essentially required starting from scratch.
Legacy Data Migration
Third-party platforms held critical financial data that needed to move into a modern ecosystem without any loss, duplication, or downtime.
Data Quality at Scale
Incoming data from multiple sources varied in format and quality, with no automated mechanism to validate consistency or completeness before processing.
Slow Customer Onboarding
Onboarding new enterprise clients took up to six months due to manual data ingestion and environment setup processes that had to be rebuilt each time.
Key Challenges Faced
- High-risk data migration from third-party platforms with zero tolerance for duplication or data loss.
- No automated data validation — manual quality checks were inconsistent and time-consuming.
- Customer onboarding took up to six months due to bespoke pipeline builds for each new client.
Infrastructure provisioning was manual, slow, and difficult to replicate across environments.
The Solution – How Infilgence helped?
Infiligence designed and built a modern data engineering platform from the ground up, focused on making the company's data operations faster, more reliable, and ready to scale across every new client it onboards.
The first step was establishing a secure, high-throughput data landing zone — a dedicated intake layer that could absorb large volumes of data from third-party platforms without compromising the integrity of the existing ecosystem. From there, a scalable ETL framework was built to ensure every dataset coming in met the company's schema, validation, and processing standards before moving downstream.
To keep data fresh without requiring full reloads, near real-time Change Data Capture pipelines were implemented to pick up delta changes from legacy systems as they happened — reducing latency and keeping downstream systems current.
Environment provisioning, previously a slow manual process, was fully automated using Terraform — allowing the team to spin up client-specific environments quickly and consistently. And to close the loop on data quality, an automated validation pipeline was built using Deequ, running checks for consistency, completeness, and accuracy across every data flow without any manual intervention.
What used to take six months to set up for a single client now takes under four weeks — and the same pipeline runs for the next one without any rework. That's the kind of scale we were looking to build.
Key Results
- 98% data transfer success rate — near-perfect migration accuracy with zero duplication or loss at go-live
- 100% reusable platform — the same pipeline architecture scales across 20+ enterprise use cases without redesign
- 80% faster customer onboarding — reduced from 6 months to under 4 weeks, accelerating time-to-revenue
- 60% efficiency gain — manual validation effort cut through automated Deequ-based quality checks
- Real-time data availability across multiple geographic regions for financial services and fraud detection workflows
