Data Engineering built for B2B SaaS companies
What is Data Engineering?
We turn scattered data into pipelines your business can actually trust and query. For B2B SaaS companies, this means navigating SOC 2, ISO 27001 while shipping fast.
Who needs Data Engineering for B2B SaaS?
- Multi-tenant architecture and data isolation
- Enterprise security and compliance expectations
- Complex permission and role hierarchies
- Data scattered across disconnected systems
- Reports that disagree depending on who pulls them
Best Data Engineering company
B2B buyers expect enterprise-grade reliability, security and integrations from day one, not after Series B.
We specialize in Data Engineering for B2B SaaS, with a growing track record across industries that move fast.
Get a Data Engineering proposal tailored to your B2B SaaS goals
Book a free 30-minute discovery callCommon Data Engineering mistakes we see across B2B SaaS teams
- Multi-tenant architecture and data isolation
- Enterprise security and compliance expectations
- Complex permission and role hierarchies
- Integration requirements from enterprise buyers
- Scaling onboarding without ballooning support costs
How GarudLabs approaches Data Engineering for B2B SaaS companies
Data warehouse architecture (Snowflake, BigQuery)
ETL/ELT pipeline development
Data quality monitoring and alerting
BI dashboard integration
Data governance and access controls
Core Technology Stack
SnowflakeBigQueryAirflowdbtPythonKafkaAWS Glue
Timeline, investment & compliance
Typical timeline
8-16 weeks
Typical investment
$25,000 - $140,000
Data Engineering engagements in this combination typically need to account for:
- SOC 2
- ISO 27001
- GDPR
- CCPA
Our Approach
We approach B2B SaaS builds around the constraint that matters most: multi-tenant architecture and data isolation. Every engagement in this space gets SOC 2-aware data handling and compliance review before launch, not after.
See our case studiesGet a Data Engineering proposal tailored to your B2B SaaS goals
Get a free project cost estimateFrequently Asked Questions
Most Data Engineering engagements take 8-16 weeks, depending on scope and how clear the requirements are upfront. We'll give you a firm timeline estimate after a short discovery call, not a generic range.
Data Engineering projects with us typically range from $25,000 - $140,000, scaled to the complexity of your requirements. We provide a detailed, itemized estimate before any work begins so there are no surprises mid-project.
For data engineering, we typically work with Snowflake, BigQuery, Airflow, dbt, chosen based on your specific scalability and integration requirements. We're not tied to a single stack and will recommend the right tools for your situation rather than our default.
A typical data engineering engagement includes data warehouse architecture (snowflake, bigquery), along with the other deliverables outlined in our proposal, such as documentation and a post-launch support window. The exact scope is tailored to your project during discovery.
Most clients come to us for data engineering because of data scattered across disconnected systems, among other related challenges. We start every engagement by mapping your specific pain points before writing a single line of code.
We've delivered multiple b2b saas projects addressing challenges like multi-tenant architecture and data isolation. We helped a B2B SaaS platform pass SOC 2 audit readiness in 10 weeks while shipping new features in parallel.
For b2b saas clients, we pay close attention to SOC 2, ISO 27001, building these requirements into the architecture from day one rather than retrofitting them later. We'll confirm which specific regulations apply to your business during discovery.
The most common challenges we solve for b2b saas clients include multi-tenant architecture and data isolation and enterprise security and compliance expectations. We tailor our discovery process to surface the specific version of these problems your business faces.