Data Warehouse Development | Snowflake BigQuery
Data warehouse development builds the single agreed source of truth that every report, dashboard, and ML model draws from. It brings together data from ERP, CRM, product databases, and SaaS tools, applies consistent business logic, and makes the combined dataset queryable by analysts and BI tools without touching production systems.
We design and build data warehouses on Snowflake, BigQuery, Redshift, or Databricks. Schema design, data modeling with dbt, pipeline integration, orchestration, and the semantic layer that makes data accessible to people who are not data engineers. Scoped and priced as one engagement.
Warehouse on Snowflake, BigQuery, or Redshift, chosen for your workload and cost profile
dbt-based transformation layer with version-controlled, tested SQL models
Semantic layer that defines business metrics consistently across every report
Historical data migration from existing data stores, spreadsheets, and legacy databases
Recent outcomes
Voice AI · Research
6× deeper insights
Text-based interviews converted to automated phone calls
AI Automation · Ops
20k+ txns day one
Manual invoice OCR across 40+ gas stations
Loyalty · Retail
1,062 users in 4 weeks
SuperValu & Centra loyalty platform with receipt validation
SaaS · Logistics
2,000+ shipments yr 1
Multi-carrier shipping hub for Indonesian eCommerce
The problem
When your leadership team asks for the same metric, does every department produce a different number because each team pulls from a different source?
How much of your data team's time goes into explaining why two reports don't agree rather than answering new questions?
Short answer
RaftLabs builds data warehouses on Snowflake, BigQuery, Redshift, and Databricks, with dimensional schema design, a version-controlled dbt transformation layer, a semantic layer for consistent metrics, and pipeline integration from ERP, CRM, and SaaS sources. A core warehouse across 3 to 5 source systems ships a working v1 in 8 to 12 weeks, typically $30,000 to $80,000.
Key takeaways
Trusted by
Every report, dashboard, and business decision eventually depends on a consistent, queryable data layer. Without a warehouse, analysts query production databases directly (risking performance issues and inconsistent results), pull manual exports from each system, or build one-off scripts that become unmaintainable. The result is a different revenue number from every team, a data definition that lives in someone's head rather than the codebase, and reporting that can't scale as the business grows.
Gartner projected that 75% of all databases would be deployed on or migrated to a cloud platform, with almost none returning on-premise (Gartner). Most data teams have already accepted that on-premise stores and ad-hoc scripts can't keep pace with the volume and variety of sources a modern business generates.
A data warehouse solves this by creating a single layer where all source systems land, business logic is applied consistently, and analysts and BI tools can query without understanding the operational structure of each upstream system. On a modern cloud platform we default to an ELT approach: land raw source data first, then transform it inside the warehouse with dbt, so you can re-run logic when a business definition changes without re-extracting from source. Building that layer is a design and engineering project. We handle the architecture decisions, the physical build, the transformation models, the orchestration, and the handoff to the team that maintains it.
Capabilities
Platform assessment based on your existing cloud infrastructure, data volume, query patterns, team SQL proficiency, and cost constraints, documented before procurement. Snowflake for variable concurrency and multi-cloud, BigQuery for GCP-native organisations, Redshift for large predictable AWS workloads, Databricks when data science and analytics share data. Setup covers private connectivity, least-privilege IAM, environment separation, and the resource monitors and cost alerts that prevent runaway query bills.
Dimensional data modelling for BI-optimised query performance: fact tables at the most granular business event, surrounded by dimensions that support any grouping without schema changes. The model is designed around your actual business questions, and every fact table's grain is documented so analysts know what a COUNT(*) means. A three-layer architecture separates raw source data, cleaned staging models, and business marts that encode agreed rules like revenue recognition once, not per analyst.
Every business transformation written as modular SQL, version-controlled, code-reviewed, and deployed via CI/CD instead of applied manually in a console. Materialisation strategies match data volume: incremental processing for large fact tables, full refresh for small dimensions. Tests catch nulls, duplicates, and broken relationships on every pull request, and generated documentation publishes the model DAG and column descriptions that stay accurate because they come from the code.
Scheduled orchestration runs pipelines and transformations in the right order, retries on failure, and alerts when a load is late or a model breaks, so stale numbers never reach a dashboard unnoticed. Governance covers least-privilege access by role, column-level controls on sensitive fields, and lineage from each report back to its source columns, so an auditor or analyst can trace where any number came from. Deeper anomaly checks connect to our data quality management service when a build needs them.
Centralised metric definitions so every business metric has one definition, one calculation, and one number, whichever BI tool or analyst asks. Definitions cover the concepts that produce disagreements, revenue, churn, customer acquisition cost, lifetime value, each documented with a plain-language calculation, the SQL formula, edge cases, and a responsible data steward. Changes go through review and pull request, so a metric never shifts unannounced.
Migration of historical records from legacy databases, spreadsheets, CSV archives, and deprecated data stores into the new warehouse. Data is cleaned and standardised during migration so historical records conform to the same schema and business logic as current records. Reconciliation reports confirm that migrated totals, revenue, order counts, customer counts, match the source systems within an agreed tolerance.
Connection setup for the BI tool your team already uses, with the semantic layer exposed so analysts build dashboards from business-defined metrics rather than raw warehouse tables. Query pattern guidance keeps reports efficient at warehouse scale without triggering expensive full-table scans. Documentation of the data model, mart layer, and metric definitions is delivered to the team that maintains the warehouse after the engagement ends.
Tell us your source systems, reporting use cases, and where analysts waste time today because data isn't consistent. We'll scope the warehouse and give you a fixed cost.
Data Engineering Services, full data engineering capability overview
ETL Pipeline Development, pipelines that move data from source systems into your warehouse
Real-Time Data Pipelines, streaming ingestion for data that needs to be current
Data Quality Management, validation and monitoring for data entering the warehouse
Stay on topic
Proof
UrShipper's fifth attempt at a multi-carrier shipping platform processed 2,000 shipments across 70+ countries in year one
Read the case study
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Read moreSnowflake is the most flexible choice, it separates storage from compute, scales independently, and works well across cloud providers. BigQuery is the right choice if your organization is already in Google Cloud and you want to avoid data movement costs. Redshift performs well for large-volume SQL workloads if you're already on AWS and your access patterns are predictable. Databricks makes sense when your warehouse workloads and ML workloads share the same infrastructure. We assess your existing cloud infrastructure, data volume, team SQL proficiency, and cost constraints before recommending a platform.
dbt (data build tool) is a transformation framework that lets you write data transformations as SQL SELECT statements, version-control them in git, test them automatically, and generate documentation from the model definitions. For most data warehouse projects it is the right tool for the transformation layer because it makes transformations reproducible, testable, and auditable. The alternative, ad-hoc transformation scripts or stored procedures, creates a transformation layer that is hard to test, hard to change, and impossible to document systematically. We use dbt as the default transformation tool and will tell you if your project is simple enough not to need it.
A warehouse covering 3 to 5 source systems with core entity models and BI tool integration typically ships a working v1 in 8 to 12 weeks, which you then expand as new sources and metrics are added. A more complete build with semantic layer, historical data migration, and a full dbt transformation suite typically takes 12 to 20 weeks. The variables are source system count and complexity, data quality issues in those systems, number of business entities to model, and whether historical migration is in scope.
A warehouse designed for change uses version-controlled dbt models as the transformation layer. When a business definition changes, for example, how 'active customer' is defined, you update one model, run the tests, and the change propagates consistently to every downstream report. The semantic layer ensures that metric changes are applied once rather than across every dashboard. We build warehouses with a clear separation between raw data, cleaned staging models, and business-logic marts so that changes in one layer don't cascade unpredictably into others.
Work with us
We scope Data Warehouse Development in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.