[ Snowflake and dbt ]
One Trusted Source of Numbers for Every Team
We set up Snowflake with Fivetran or Airbyte ingestion, dbt models, Airflow orchestration and BI on top, so finance, product and sales stop arguing about whose spreadsheet is right.
ServedDashboards, reverse ETL, AI features42 models- Modeleddbt marts with tests and docs180 tables
- StagedCleaned, typed and deduplicated1.2 bn rows
- RawFivetran, Kafka and file loads26 sources
Example warehouse layers and volumes.
Snowflake separates storage from compute and runs as a managed service, which lets a small data team support a fast-growing company.
- 01
No servers to babysit
There are no indexes to rebuild, disks to resize or vacuum jobs to schedule. Your engineers spend time on models, not on the warehouse itself.
- 02
Separate compute per workload
Loading, dbt runs and dashboards each get their own virtual warehouse, so a heavy transform never slows down the CFO's report.
- 03
Spend you can control
Warehouses suspend when idle and bill by the second, and resource monitors cap credits per team before a bad query becomes a bad invoice.
- 04
Safe experiments
Zero-copy clones and Time Travel let us test a new model or migration on a full copy of production data and roll back in minutes.
- 05
Python and AI next to the data
Snowpark and Cortex AI run Python, ML and LLM functions inside Snowflake, so sensitive data does not leave the governed environment.
- 06
Sharing without file exports
Secure data sharing gives partners, customers or a sister company live read access to selected tables without CSVs or SFTP jobs.
Snowflake and Data Stack Services
We cover the full path from raw source data to a dashboard someone opens every Monday.
- 01Raw layer
Snowflake setup and migration
Account design, roles, warehouses and environments, plus migration from Redshift, BigQuery, Postgres or SQL Server with row-count checks.
- 02Raw layer
Ingestion pipelines
Fivetran or Airbyte for SaaS sources, Snowpipe Streaming for product events, and custom Python loaders for APIs nobody else supports.
- 03Modeled layer
dbt modeling and testing
Staging, intermediate and mart layers in dbt with tests, documentation, CI on every pull request and clear ownership per model.
- 04Staged layer
Orchestration with Airflow
Airflow or Dagster DAGs that sequence loads, dbt builds and reverse ETL syncs, with retries, SLAs and Slack alerts.
- 05Served layer
BI and semantic layer
Shared metric definitions and dashboards in Looker, Power BI, Tableau, Sigma or Metabase that match finance's numbers.
- 06Staged layer
Governance and cost tuning
Masking policies, row access, audit history and a credit review that right-sizes warehouses and rewrites the most expensive queries.
- 07Served layer
AI on warehouse data
Cortex search, Snowpark ML features and RAG pipelines that answer questions from governed tables instead of copied exports.
What We Build on the Modern Data Stack
Data projects with a named owner and a decision attached to them, not dashboards nobody opens.
01SaaS revenue and churn reporting
MRR, expansion, churn and cohort retention built from Stripe, the product database and the CRM, reconciled against finance every month.
02Product analytics warehouse
Event streams from web and mobile joined with accounts and billing, so product teams can query funnels in SQL instead of exporting from tools.
03Customer-facing analytics
Usage dashboards embedded in your app, backed by Snowflake with row access policies so each tenant sees only its own data.
04AI-ready data foundations
Clean, documented tables that feed lead scoring, forecasting and RAG assistants, with lineage so you know where every answer came from.
Snowflake Compared With BigQuery and Databricks
All three are capable platforms. We recommend based on your cloud, your team's skills and how much ML work sits on top.
| Criteria | Snowflake | BigQuery | Databricks |
|---|---|---|---|
| Best fit | SQL analytics, BI, data sharing | Teams already on Google Cloud | Heavy ML and Spark workloads |
| Pricing model | Credits per warehouse second | Per TB scanned or reserved slots | DBUs plus your cloud compute |
| Cloud choice | AWS, Azure, Google Cloud | Google Cloud | AWS, Azure, Google Cloud |
| Admin effort | Low | Very low | Medium to high |
| Python and ML | Snowpark and Cortex, improving fast | BigQuery ML and Vertex AI | Strongest, notebooks and MLflow |
| Workload isolation | Separate warehouse per team | Reservations and slot assignments | Separate clusters per job |
Need data engineers?
Add senior Snowflake, dbt and Airflow engineers to your data team, with engineers starting within 48 hours, full-time or part-time.
Talk to a Data LeadOur Modern Data Stack
Tools we pick per layer based on your sources, team size and budget. You do not need all of them.
- Snowflake
- Snowpark
- Cortex AI
- Dynamic Tables
- Apache Iceberg
What Clients Say About Working With Us
“I had the pleasure of working with Sajal Tech on a travel website, and it was an incredible experience from start to finish. They demonstrated professionalism, clear understanding of requirements, and excellent communication throughout.”
Snowflake Questions
A first warehouse with a handful of sources, core dbt models and a few dashboards starts from $8,000 at a fixed price. Migrations from an existing warehouse and larger multi-team platforms are quoted after a free scoping call.
Usually 2 to 3 weeks for the first sources and a reconciled revenue or usage dashboard. We then add sources and models in two-week sprints with a live demo each week.
It depends on data volume, refresh frequency and how many people query it. Many startups spend a few hundred dollars a month at first. We set auto-suspend, right-size warehouses and add resource monitors on day one so costs stay visible.
We strongly recommend it. dbt puts your business logic in version-controlled SQL with tests and documentation, so a metric change goes through code review instead of an untracked edit in a BI tool.
Fivetran is the low-maintenance choice for common SaaS sources if the usage-based price fits your volume. Airbyte, self-hosted or cloud, costs less at scale and handles custom connectors, but needs more care. We often mix both.
Sometimes. If you only run Fivetran and dbt on a schedule, dbt Cloud jobs or Dynamic Tables may be enough. We bring in Airflow or Dagster once you have dependencies across tools, custom loaders or reverse ETL.
Yes. We move schemas and history, translate SQL, run the old and new warehouses side by side and compare row counts and key metrics before cutover. Dashboards switch only after the numbers match.
Role-based access with least privilege, masking policies for personal data, network policies and SSO. For HIPAA or GDPR workloads we pick the right Snowflake edition and region and document data flows for your auditors.
Yes. Cortex AI offers LLM functions and search inside Snowflake, and Snowpark runs Python models next to the data. For more complex agents we expose governed tables to an external RAG or agent service.
You do. Snowflake and tool accounts are in your name, and the dbt project, DAGs and documentation live in your repository, with 30 days of post-launch support.
Let's build your next product together
Book a free strategy call and leave with a clear plan and estimate. No commitment.
- 01Pick a time that suits you
- 0230 minutes on scope, stack, timeline and budget
- 03Fixed-price proposal, NDA on request