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Snowflake Consulting

Snowflake, engineered for production.

dbt-native modeling, ELT pipelines, cost guardrails, reverse ETL — production data platforms that the warehouse bill does not surprise.

Why Snowflake

Compute and storage decoupled. Cost discipline as engineering, not a quarterly fire drill.

60M+

Events processed per day across 4 tenants

5-min

SLA on analytical metrics off the warehouse

4 days

Tenant onboarding, down from 6 weeks

~30%

Of what 4 single-tenant builds would have cost

Services

What we build on Snowflake.

Eight Snowflake engagement shapes. Architecture, modeling, cost, ML feature store, reverse ETL — pick the one that fits.

Snowflake architecture & setup

Account, warehouse, role, and resource-monitor design from day one — RBAC done properly, cost guardrails before the bill, multi-environment patterns.

dbt modeling + transformation

dbt project structure, source freshness, exposures, semantic layer, CI on PRs. The model layer that turns raw data into trusted tables.

ELT pipelines (Airbyte, Fivetran, custom)

Source ingestion from Shopify, NetSuite, GA4, Stripe, and 100+ SaaS via Airbyte / Fivetran — or custom Python connectors when the off-the-shelf doesn't fit.

Cost optimization

Warehouse right-sizing, query tuning, search-optimization service, materialized views, clustering keys. Warehouse cost engineering baked into the model layer, not a quarterly review.

Reverse ETL into ops tools

Hightouch / Census wiring back into Klaviyo, Salesforce, ad platforms — the warehouse becomes the source of truth for ops, not just BI.

BI + semantic layer

Looker, Metabase, Tableau on top of dbt's semantic layer. Or wire MetriQ in for natural-language analytics.

ML feature store on Snowflake

Snowpark + feature store patterns when ML training and inference need warehouse-grade data.

Migration to Snowflake

From Redshift, BigQuery, Postgres, or on-prem warehouses — phased, with parity validation before cutover.

Stack deep dive: MetriQ

Where we built

Engagement record

Multi-tenant retail data platform, on Snowflake.

60M+Events processed per day across 4 tenants — 5-minute SLA on analytical metrics, tenant onboarding cut from 6 weeks to 4 days

Read the case study

How an engagement starts

Three steps to a partnership

01

Intake call

30 minutes. We listen, you talk. No deck.

02

Diagnostic

We audit the surface, name the bottleneck, propose a path.

03

Kickoff

Senior engineer in your standup by week two.