The client adopted dbt to modernize their data operations — the right tool for the job. But it was implemented without a solid data foundation underneath. Views layered on top of views. Queries hit the warehouse like a sledgehammer when they should have been surgical. Within two days of going live, the client burned through their entire monthly Snowflake credit allotment.

Two days. A full month's budget. Gone.

And the data coming out the other end wasn't even accurate. Key business metrics were being double-counted — meaning the numbers leadership was acting on were wrong.

• No data foundation. Nested views cascaded compute costs with every query. Performance collapsed under the weight of its own complexity.
• No refresh optimization. Data was being reprocessed far more frequently than any business use case required — 10x the work for 1x the value.
• Logical errors in the metrics layer. Key business metrics were being double-counted. The client was making decisions based on numbers that were structurally wrong.

• Redesigned the data model from the ground up — replaced the layered view architecture with a streamlined structure built for efficiency and scalability
• Tuned refresh rates to match actual business needs — not everything needs to be real-time
• Audited and corrected every logical error in the metrics layer — the client's numbers were accurate for the first time

• 89.3% reduction in execution time — operations that previously crushed the warehouse now run efficiently within allocation
• Monthly Snowflake costs brought well below budget — what burned through credits in 48 hours now runs comfortably
• Business metrics corrected and trusted — leadership has numbers they can act on without second-guessing
• Team unblocked — engineers are building new capabilities instead of fighting infrastructure

The client's Azure Data Factory environment powered their Enterprise Data Warehouse — daily and hourly pipelines moving large volumes of data from raw to structured zones and into SQL. It worked. But it was expensive and slow.

• Daily loads averaged 551 minutes — over 9 hours
• Hourly loads averaged 299 minutes — almost 5 hours per run
• ADF compute represented ~90% of total monthly cloud costs

No radical re-engineering — the existing pipelines encoded years of business logic.

• Database & Process Tuning — Optimized key handling, deferred FK enforcement, cut insert contention
• Large Process Optimization — Runtime: 28m22s → 24m56s. Azure IR: 28m22s → 20m9s
• Non-Data Movement Activities — Moved schema swaps and index management off expensive Azure IR onto SHIR
• Visibility Enhancements — Performance monitoring with rolling 30-day views
• Concurrency & Scheduling — Wait/suppress logic, eliminate wasted extracts

• Major cost reduction — The single largest cost driver (~90% of spend) cut significantly
• 10–28% runtime reductions across large pipelines. Parallelization improved throughput
• Edge-case failures eliminated — Data available within the same business day, every time
• Framework extends to additional pipelines without disruption

The client operated on ephemeral Spark clusters and Redshift. Hundreds of interdependent scripts. ETL that routinely exceeded 24 hours — for a process that was supposed to run daily. A flat-table data lake model riddled with inaccuracies and duplications.

When a number was wrong — and numbers were frequently wrong — nobody could trace it back through hundreds of scripts to find where the error was introduced. Troubleshooting wasn't difficult. It was effectively impossible.

The infrastructure hadn't been designed. It had accumulated.

Migrated the client to managed Snowflake with dbt for transformations and Apache Airflow for orchestration — a rethinking of the data architecture from the ground up.

• Spark cluster sprawl replaced with a managed, governed environment
• Flat-table model replaced with a proper layered architecture
• Hundreds of interdependent scripts replaced with testable, documented transformations

• ETL reduced from 24+ hours to minutes — daily processes now complete well within their window
• Data accuracy issues eliminated at the architectural level — not patched. Eliminated.
• Operational costs reduced significantly — managed infrastructure replaced expensive ephemeral cluster overhead
• Engineers troubleshoot in minutes instead of days