Case Studies
Real Problems. Real Results.
How we've helped companies turn chaotic data infrastructure into reliable, high-velocity platforms.
Turning Unreliable Analytics Into a Trusted Single Source of Truth
The Challenge
A mid-size company had grown fast and ended up with multiple "sources of truth." Finance, product, and operations dashboards showed different numbers for the same KPIs, pipelines failed regularly, and every change took weeks because nobody trusted what would break downstream.
What We Delivered
We stabilized and modernized their analytics platform by redesigning the data flow into clear layers (Raw → EDW → Data Marts). We implemented automated data quality checks, added monitoring and run-level logging, introduced column masking for sensitive fields, and optimized the warehouse for faster queries and lower cost. The result was a governed, reliable platform where KPIs are defined once and reused everywhere.
Team & Timeline
8 weeks
3 engineers (Data Architect + 2 Data Engineers)
Technologies
Business Impact
- All teams aligned on one set of numbers
- Reporting became trustworthy across the org
- Changes released confidently on a predictable cadence
- Faster decisions in finance and product planning
Building a Scalable Lakehouse for Multi-Source Ingestion and Long-Term History
The Challenge
A client needed a new platform to ingest many external and internal sources into one analytics foundation. Their main pain was that each integration was a custom effort, schema changes broke pipelines, and historical tracking was inconsistent—making audits, trend analysis, and customer/asset history hard to trust.
What We Delivered
We built a lakehouse platform with a repeatable ingestion framework and standardized source onboarding. We implemented a layered architecture (Raw → Staging → ODS) and used a Data Vault–style integration layer to preserve history and make the model resilient to source changes. We added a reprocessing strategy, batch/run logging, and monitoring so operations are transparent and recoverable.
Team & Timeline
10 weeks to MVP
4 engineers (Data Architect + 3 Data Engineers)
After MVP, new source onboarding: ~1 week each
Technologies
Business Impact
- New sources onboarded in ~1 week vs. custom builds
- Schema changes stopped causing widespread failures
- Reliable historical tracking for analytics and compliance
- Stable foundation for BI and downstream use cases
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