Helping buyers make confident home loan decisions, backed by data

Operating alongside the well-known Aussie Home Loans brand, Lendi plays a central role in closing the gaps between borrowers, brokers, and lenders. Its model blends automation, transparency, and expert advice to deliver a streamlined alternative to traditional mortgage broking.

As the business has grown, data and technology have become increasingly central to how Lendi operates at scale. Consequently, Lendi has set an ambition to become a fully AI-native organisation in 2026.

“Becoming AI-native is a critical opportunity for us to break away from the pack by embedding AI at the core of how we operate – across our workflows, decisions and customer experiences,” says David Hyman, co-founder of Lendi.

Visit Lendi to learn more.

BUSINESS CHALLENGES

Lendi is a data-driven business, with strong demand for data insights spanning executive leadership, operations, finance, and marketing.

As this demand grew, it put increasing pressure on engineering and platform teams, who were spending time maintaining a self‑hosted analytics environment built on dbt Core and external orchestration, which was slowing Lendi’s ability to deliver new insights to the business and keep pace with growing demand.

Platform instability led to failed pipelines and stale reports – preventing access to data when teams needed it to make decisions – while reduced developer velocity caused analytics work to back up.

At the same time, complexity in the development environment made it harder for Lendi’s data teams (data engineering, analytics, data science, and AI engineering) to contribute to dbt pipelines effectively, limiting the organisation’s ability to scale its analytics function.

THE DATA ARMY SOLUTION

Lendi implemented dbt in partnership with Data Army, configuring the platform using Terraform and integrating with Lendi’s identity management platform, Microsoft Entra, to support role‑based access control.

Code repositories were integrated with Bitbucket, with deployments co‑managed alongside existing tooling. This approach helped standardise environments and reduce operational overhead as dbt was rolled out across teams.

The migration was completed with no downtime, with the second phase finished within a week. The resulting setup supports multiple projects, teams and jobs serving a range of analytical needs across the organisation.

PROJECT OUTCOMES

Lendi assessed the impact of dbt across delivery velocity, data quality, and cost efficiency. The team look at a combination of indicators to understand how the platform was improving how they worked:

These included:

• Time to onboard new data engineers. Reduced ramp-up time due to consistent project structure, documentation and CI.
• Time to deliver new models or changes. Faster development cycles with fewer production incidents.
• Test coverage and freshness SLAs. Increased automated testing and clearer expectations around data availability.
• Reduction in pipeline failures and rework. Less time spent debugging downstream issues.
• Platform cost optimisation. More efficient warehouse usage driven by better model design and incremental patterns.
• Data reuse. Higher reuse of curated models rather than rebuilding logic in downstream tools.

Data Army is a dbt Visionary & Consulting Services Partner. 

This case study was originally published by dbt.