Modern Data Warehousing
We design, build and operate modern data warehouses on Snowflake, BigQuery, Databricks or open lakehouse stacks, with dbt-native modelling, semantic layers and governance baked in.
Warehouses that scale insight, not just storage.
A warehouse only earns its keep if the answers it gives are trusted, fast and cheap. Our approach treats warehouse modernization as three problems: model the business correctly, govern the metrics ruthlessly and tune the engine relentlessly. Done well, the warehouse becomes the most-trusted system in the company. Done badly, it becomes the most-expensive question-mark in the budget.
Model the business, not the source
We model around concepts your business already uses (orders, customers, periods), never around the quirks of the upstream system schema.
Metrics as code
Definitions live in dbt and the semantic layer, version-controlled, peer-reviewed and propagated everywhere. No more "depends who you ask" answers.
Cost transparency
Per-team, per-pipeline, per-dashboard cost dashboards. Engineers and analysts see the price of every query, every materialization, every weekend job.
Why warehouse modernization is back on the 2026 roadmap.
After a decade of warehouse-everywhere proliferation, three forces are pulling enterprises back to discipline, governance and cost rigour.
Snowflake, Databricks and BigQuery spend has crossed seven figures at thousands of companies. Cost rigour is no longer optional.
A 2025 industry study confirmed what most data leaders suspected, the majority of warehouse spend is waste that better governance reclaims.
AI assistants need a single, governed metric layer to answer business questions reliably. The warehouse is suddenly back in the spotlight.
Data Warehousing (DW) services we offer.
Each item below is a discrete, measurable workstream we own end-to-end, with senior engineers, real timelinesand the test coverage to back it up.
Dimensional and lakehouse modelling
Kimball, Data Vault 2.0 or one-big-table, the right model for the right problem, never a religion. Schemas your analysts can navigate without training.
dbt-native transformation
Modular SQL with tests, docs, lineage and CI. Refactors stop being scary, breakages caught before merge, never after.
Semantic layer
Cube, dbt Semantic Layer or LookML, one definition of "revenue", "active user", "margin", queryable from BI, AI and direct SQL alike.
Data contracts and quality gates
Schema-versioned, owner-attributed datasets with SLA-backed freshness and accuracy. Data breakages caught at the producer, never the consumer.
Performance and cost tuning
Clustering, partitioning, materialization strategy and warehouse sizing. Snowflake credits and BigQuery slot-hours cut 30-60% within 90 days.
Reverse ETL and activation
Hightouch, Census or custom syncs that push warehouse data into Salesforce, HubSpot, Braze and ad platforms. Data finally reaches the people who use it.
We're fluent in your stack.
Vendor-agnostic by design. We pick the right tool for the problem in front of us, not the one our partner discounts apply to.
Real engagements. Real numbers.
Cut Snowflake bill 41% in 60 days
Warehouse rightsizing, materialization tuning and a "kill the orphan dashboards" review. Same insights, two-fifths cheaper, every month forever.
Six reasons enterprises run Data Warehousing (DW) with Infivit.
Built for the 2026 reality of Data Warehousing (DW): the actual buyer pain, the actual technical constraints and the actual outcomes that matter, not generic data buzzwords.
30-60% lower warehouse bill in 90 days.
Rightsizing, materialization tuning, query rewrites and orphan-dashboard culls. Most engagements pay for themselves before the first quarterly review.
Semantic layer, metrics-as-code.
Definitions of revenue, customer, margin, all version-controlled, peer-reviewed and propagated to BI, AI and APIs. The "depends who you ask" era is over.
Sub-5-minute SLAs for the metrics that matter.
Streaming for revenue-critical, micro-batch for the rest. Producers and consumers agree on freshness in writing, drift caught at the boundary.
Column-level lineage, source to dashboard.
Every metric traceable to the bytes it came from. Audit prep stops being a fire drill, regulator questions get answered in minutes, not weeks.
SOX, DPDP, GDPR, ready on day one.
Time-travel queries, role-based access, PII tagging and right-to-be-forgotten workflows. Compliance becomes a feature of the warehouse, not a separate project.
Open formats, portable models.
dbt models, Iceberg tables and standard SQL. Swap warehouses as a refactor, never a rewrite. Negotiate with Snowflake from a position of leverage.
The questions you were already going to ask.
Got a data warehousing (dw) problem?
Let's ship the fix.
A 30-minute call with one of our senior engineers, no slideware, no scoping doc. You leave with a concrete view of what the first 30 days look like.