Helping buyers make confident home loan decisions, backed by data
Lendi is Australia’s leading digital-first home loan platform, operating under Lendi Group, one of the country’s fastest-growing fintechs. For the millions of Australians navigating the home loan market, the process has long been slow and complex. Lendi set out to change that, combining smart technology with human expertise to help borrowers compare, choose, and apply for mortgages with confidence.
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, CEO and co-founder of Lendi.
Growing analytics demand outpaced a self-hosted platform
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.
Moving from self-hosted to dbt platform
As part of a broader effort to simplify the data stack, Lendi evaluated the full cost of continuing to self‑host dbt, including compute, ongoing maintenance, support overhead and opportunity cost. In a detailed cost comparison between self-hosting and dbt platform, dbt came out as the clear winner.
dbt offered a way to offload platform management while strengthening governance, collaboration, and development velocity. By moving to a managed platform, engineering teams could spend less time maintaining infrastructure and more time building analytics that deliver value to the business.
Lendi implemented dbt in partnership with dbt consultancy 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.
“Data Army consistently delivered exceptional value at Lendi, quickly absorbing context, integrating seamlessly with our squads, and contributing meaningful outcomes from day one,” says Frank Colubriale, Data Product and Enablement Lead, Lendi Group. “Their contribution continues to position us strongly in delivering on our AI native vision and the increasing pace of innovation across the business.”
Faster development and greater trust in data
By moving to dbt, Lendi reduced the operational overhead associated with maintaining self-hosted analytics infrastructure. Engineering teams now spend less time managing platforms (an average of 16 hours per month saved) and more time working closely with the business to deliver insights.
Standardised workflows, access controls and collaboration features increased development velocity and expanded the pool of contributors (by 10 people) to analytics development. dbt features such as the Web IDE, dbt Mesh and dbt Copilot made it easier for multiple teams to safely contribute to shared analytics pipelines and service business demand.
At the same time, trust in data improved across the organisation. Tests, documentation and lineage made data quality explicit rather than assumed, giving teams greater confidence in both upstream changes and downstream outputs. As a result, Lendi experienced fewer pipeline failures, less rework, and more reliable reporting to support decision-making.
How Lendi evaluated success
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.
What’s next
As Lendi Group works towards its AI‑native ambition, the team is focused on scaling trusted data products across more teams, enhancing automation and data quality, and preparing its data foundations for advanced analytics and AI. dbt remains the core transformation and governance layer supporting this work.
