Data Engineering built for PropTech companies
What is Data Engineering?
We turn scattered data into pipelines your business can actually trust and query. For PropTech companies, this means navigating Fair Housing Act (US), Local landlord-tenant laws while shipping fast.
Who needs Data Engineering for PropTech?
- Manual property management workflows
- Tenant communication and maintenance request friction
- Disconnected leasing and accounting systems
- Data scattered across disconnected systems
- Reports that disagree depending on who pulls them
Best Data Engineering company
Property management software needs to satisfy landlords, tenants and compliance teams simultaneously.
We specialize in Data Engineering for PropTech, with a growing track record across industries that move fast.
Start your Data Engineering project with a team that knows PropTech
Book a free 30-minute discovery callCommon Data Engineering mistakes we see across PropTech teams
- Manual property management workflows
- Tenant communication and maintenance request friction
- Disconnected leasing and accounting systems
- Smart building IoT integration complexity
- Compliance with local rental regulations
How GarudLabs approaches Data Engineering for PropTech 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:
- Fair Housing Act (US)
- Local landlord-tenant laws
- GDPR (EU)
- Building safety codes
Our Approach
For PropTech platforms, our focus is manual property management workflows without sacrificing delivery speed — architecture and code review practices are adjusted from day one for Fair Housing Act (US) and Local landlord-tenant laws.
See our case studiesGet a Data Engineering proposal tailored to your PropTech 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 proptech projects addressing challenges like manual property management workflows. We helped a property management platform reduce maintenance request resolution time by 45%.
For proptech clients, we pay close attention to Fair Housing Act (US), Local landlord-tenant laws, 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 proptech clients include manual property management workflows and tenant communication and maintenance request friction. We tailor our discovery process to surface the specific version of these problems your business faces.