Powering property management decisions with quality data
PetScreening helps property managers handle pet policies, assistance animal requests, and related compliance requirements for residential and commercial properties.
Its platform connects data across applicants, residents, and pet profiles. As the business scaled, the reliability and freshness of that data became critical not just for analytics, but day-to-day operations and the customer experience. The lost pet alert program alone depends on accurate-real-tie data to produce and deliver engraved pet tags to customers. Consequently, the business relies on the data team to connect, transform, and activate large quantities of data so that data is fresh, accurate, and trustworthy.
To meet this challenge, PetScreening relies on a data stack that includes Snowflake for warehousing, Airbyte for ingestion, Hightouch for reverse ETL, and dbt platform to transform and orchestrate data across the organization.
A data stack that struggled to keep up with the business
As demand grew across the business, stakeholders began to question whether the data team could keep up. With leadership relying on timely operational and KPI reporting, slow turnaround was starting to affect confidence in the team’s ability to support the business at scale.
Before dbt, they connected reporting directly to the product’s Postgres database, which was also the live system powering the application. Beyond the technical risk, this setup was very inefficient, straining the production system since every report ran against the same system handling live customer transactions.
PetScreening tried a low-code transformation tool. It worked initially, but as data needs grew across multiple business domains, it could not deliver the governance, robustness and delivery speed required.
In a business where leadership relies on timely operational and KPI reporting, slow turnaround quickly became a credibility issue. Data requests often had to wait until someone had time, and business stakeholders began questioning whether the data team could ship improvements quickly enough to keep up with demand.
Building scalable analytics without the DevOps overhead
For PetScreening, the priority was to build a foundation that would let the team deliver trusted data more quickly across the organization, without adding unnecessary operational overhead.
As a small data team, the appeal of dbt platform was straightforward: pipeline orchestration, Snowflake integration, Git-based workflows, and built-in documentation all in one solution. This effectively provided analytics engineering guardrails and operational structure without the need to build or maintain a separate DevOps layer.
They team also made a deliberate choice to build on SQL rather than a low-code approach. A SQL-based foundation meant easier hiring, simpler onboarding, and a codebase that more people across the organization could eventually read and contribute to. From day one, dbt helped establish standardization, documentation, and lineage, keeping the system understandable as it scales and reducing technical debt as it grew.
PetScreening worked with Aimpoint Digital to architect the right foundation with dbt for long-term scale.
What they built first
Initial adoption concentrated on two priorities:
- Core reporting across the business, including KPI reporting, financial reporting, reporting for operational teams like customer support and animal assistance (including workflows for reviewing emotional support animals)
- High-impact activation in parallel, with pipelines feeding campaigns and initiatives through Hightouch, plus operational workflows like the pet tag pipeline
As delivery cycles shortened, teams across the business became more ambitious. Requests that once waited for someone to have bandwidth became fast-turnaround improvements. More PetScreening employees could share SQL with analytics engineers, understand what was happening and collaborate directly. This helped demystify data work and build shared ownership.
“If you’re a data team of one, by using dbt you have a DevOps team built into that, and you’re building correct foundations from the start.” - Will Guicheney, Principal Analytics Engineer, Aimpoint Digital
From fixed batch updates to near real-time delivery
Previously, transformation pipelines ran only 3 times per day, which limited when data updates, fixes and new features could be deployed. With dbt platform, pipelines now run every 15 minutes, reducing latency between development and production availability giving teams access to fresher data.
This shift reduced the time it took for changes and insights to reach end users from hours or even a full day to near real time — enabling faster decision making and execution across the business.
Nowhere was this more visible than in PetScreening’s lost pet alert program, which alerts nearby users if a pet is reported missing, and enables customers to sign up and receive a free engraved pet tag to connect pets back to their owners. The original process relied on CSVs emailed to an external manufacturing partner, often leading to duplicate or missing data and no robust testing. Backlogs stretched into months, creating a poor customer experience.
PetScreening treated the workflow as a data problem: ingest order data, transform it into efficient models, and feed clean, deduplicated models into the tag printing process. dbt made it practical to continually adapt the pipeline as requirements change and add matching logic when orders lacked identity data. Up to 1,000 tags per day are now printed by PetScreening, with improved reliability and faster iteration as printing requirements evolve.
“We built ‘gold-level’ models in dbt – almost like a reverse ETL process. Instead of powering a dashboard, the data powered the tag printing process. The backbone is really the lineage of dbt models that allows us to clearly track how data is built at every step and trust the outputs.” — Nick Fernandez, Data Analytics Manager at PetScreening.
Scaling impact without scaling headcount
PetScreening’s five-person data team now supports six distinct business domains, more than 30 direct stakeholders, and hundreds of data users across the organization — without adding headcount.
The standardization, documentation, and deployment practices built on dbt platform are what make it possible for a small team to operate at this breadth. Without those foundations, supporting this level of demand would have required a substantially larger team.
“dbt is what let us scale our footprint without putting pressure on headcount. Without good standardization, documentation, and easy code deployments a team of our size would be immobilized,” adds Fernandez.
A foundation for scaling data and AI across the business
As PetScreening looks ahead, its investment in governed, well-modeled data is laying the groundwork for AI adoption. In fact, the team has already put this into practice. Using dbt and Snowflake Cortex, they built a pipeline that takes messy job title data from HubSpot and outputs a clean, standardized field the marketing team can use for segmentation.
The team sees two main opportunities: helping developers move faster by building on trusted models, and giving business users easier access to insights that once required technical support. Both rely on the same foundation of reliable, well-documented models and strong semantic definitions. With the right data foundations in place, PetScreening is now in a stronger position to explore AI use cases across the business.
“Having that good baseline of data models and well-defined lineage is the enabler for good results from an AI agent looking at the data,” explains Fernandez.
With dbt, PetScreening has built the infrastructure that makes that possible. Governed data powers effective AI, and AI provides consistent, trusted outputs across the business.
