Scaling Quality Engineering for a Growing FinTech Lending Platform
Transforming limited automation capability into a scalable, data-driven Quality Engineering practice. This case study shows how Anchor Quality helped mysherpas with Scaling Quality Engineering for a Growing FinTech, increasing automated coverage, speeding up regression, and enabling safer, faster releases.
Key Outcomes & Results
8 → 100+
Automated end-to-end scenarios covering core business processes.
~30 mins
Full regression execution time, providing rapid developer feedback.
Client
mysherpas
Industry
Financial Technology / Commercial Lending
Website
Engagement
Long-term consulting engagement 2023 - 2026
Role
QA Automation Engineer / Quality Engineering Lead
Delivery Model
Remote Consulting Engagement
10–12
Releases supported per month with high engineering confidence.
Note: These figures describe engagement outcomes only and are not guarantees of future performance.
About mysherpas
mysherpas is a dynamic FinTech lending platform dedicated to transforming commercial finance through innovative digital solutions. As a growth-stage company, they specialize in providing streamlined, technology-led credit access for small to medium-sized enterprises.
The Challenge
When the engagement began, mysherpas was at an important stage of product growth. The engineering team was expanding, the platform was becoming more feature-rich, and customer adoption was increasing.
- A small automated test suite consisting of approximately eight automated scenarios.
- Tests with dependencies between scenarios, where one test relied on another to create required data.
- Limited flexibility and maintainability within the existing automation approach.
- Significant reliance on manual regression testing.
- Limited automated coverage across key customer workflows.
Reduced confidence when validating frequent releases. The objective was to establish a scalable quality engineering capability that could support faster delivery while maintaining confidence in software quality.
Objectives
Automate Regression
Transition from 95% manual regression to a high-coverage automated model.
Test Data Efficiency
Implement data-driven testing to eliminate manual data setup and environment dependency.
Shift Testing Left
Embed quality checks earlier in the SDLC to identify defects before they reach staging.
Culture of Quality
Foster engineering ownership of quality through coaching and shared tooling.
Scalable Framework
Build a modern, maintainable automation framework supporting multiple engineering squads.
Risk Mitigation
Stabilize critical payment and lending flows through rigorous automated validation.
Improve Release Speed
Reduce feedback cycles from days to minutes to support a higher release cadence.
The Approach
Automation Framework Transformation
Transitioned to a modern, high-performance Selenide framework, supported by REST Assured for API testing. This move significantly reduced execution times and improved developer experience for the QE team.
Independent and Data-Driven Testing
Decoupled tests from brittle UI dependencies by integrating API-driven setups. Implemented data-driven testing strategies using JSON-based configurations to cover complex commercial lending personas and financial rules.
Optimising Test Data Management
Engineered automated SQL and API utilities for on-demand test data generation and teardown. This eliminated cross-test data pollution and enabled reliable parallel execution across multiple testing environments.
Expanding Quality Engineering Capabilities
Parallel Testing Strategy
Allure Reporting
Docker based test execution
CI/CD Pipelines
REST Assured
Aws Integration for automated reporting
Performance Benchmarking
Accessibility Testing
Shift Right Production Validation
Building Quality Into Engineering
- Direct Collaboration: Worked closely with Frontend and Backend Leads to ensure automation strategies were integrated into the broader architectural vision.
- Engineering Culture: Shifted the perception of QA from a 'testing service' to a shared engineering responsibility, promoting proactive quality checks during development.
- Knowledge Transfer: Facilitated technical sessions to upskill developers on writing and maintaining reliable end-to-end tests within the new framework.
The engagement demonstrated that scaling quality is not just about tools, but about embedding quality principles into the daily habits of the engineering team.
Results & Outcomes (Technical)
This evaluation details the specific technical milestones achieved during the framework overhaul, focusing on performance, coverage, and delivery speed.
Architectural Foundation
Implementing a robust Quality Engineering stack involved moving beyond bug detection to create a sustainable testing infrastructure. By leveraging modern frameworks, the system now provides automated gates that validate system integrity through every stage of the pipeline, ensuring stability during rapid code changes.
Framework Capacity
Deployment of a modular Selenide and REST Assured architecture, successfully scaling from 8 unstable scripts to over 100 comprehensive automated test scenarios.
Technical Metric: 1,150% increase in automated test coverage.
Efficiency in Execution
Optimization of the test execution engine resulted in a 30-minute full regression suite, significantly reducing the duration compared to manual validation cycles.
Technical Metric: Automated regression feedback loop reduced to 0.5 hours.
Deployment Velocity
Integration of automated validation gates into the CI/CD pipeline enabled a stable cadence of 10 to 12 production-ready releases every month without failure.
Technical Metric: Support for high-frequency deployment cycles.
Platform Integration
Cross-functional framework integration allows developers to execute and update end-to-end tests locally, creating a unified approach to code quality and system health.
Technical Metric: Multi-squad adoption of shared automation tooling.
The Business Case for Quality Engineering
Quality Engineering is not simply a technical investment. It is an investment in engineering capacity, delivery speed and operational risk.
For a growing software business, the cost of poor quality extends well beyond the QA team. Slow regression cycles consume engineering time, production defects create unplanned work, and unreliable releases can delay product delivery or affect customers.
The objective of this engagement was therefore not simply to automate more tests. It was to create a quality capability that could scale with the business without scaling manual effort at the same rate.
Quality as an Enabler of Growth
The commercial question is not simply “How much does QA cost?”
It is “How much engineering capacity, delivery speed and operational risk can better quality practices unlock?”
In this engagement, the transformation from limited automation to a scalable Quality Engineering approach provided the foundation for 100+ automated scenarios, approximately 30-minute regression feedback and 10–12 releases per month.
The result was a quality capability designed to scale with the business — helping the engineering organisation deliver more frequently without increasing manual testing effort and delivery risk at the same rate.
More Engineering Capacity
Automating repetitive regression work reduces the amount of QA and engineering time spent repeatedly validating existing functionality.
That capacity can instead be used to build new product capability, improve existing services and address higher-value engineering work.
Business value: More of the team's paid engineering capacity is available for work that moves the product forward.
Faster Delivery
A full regression reduced from days to approximately 30 minutes provided rapid feedback compared with lengthy manual regression cycles.
With the platform releasing 10–12 times per month, that feedback speed becomes increasingly valuable.
Business value: Faster time-to-market and greater delivery responsiveness without quality assurance becoming a constraint on release velocity.
Lower Cost of Scaling
Increasing automated coverage from 8 to 100+ scenarios created a repeatable quality capability that could support a growing engineering organisation.
The goal was not simply to increase the number of tests, but to create a testing approach that could support increasing product complexity without requiring manual regression effort to grow at the same rate.
Business value: Higher release capacity without a proportional increase in testing overhead.
Reduced Cost of Failure
Defects identified late in the development or release cycle can require additional engineering effort, delay releases and create unplanned work.
Reliable automated coverage provides earlier feedback on critical functionality and reduces reliance on manual detection immediately before or after release.
Business value: Less unplanned engineering work, fewer avoidable production issues and lower operational disruption.
Facing Similar QA Challenges?
If your growing engineering team is struggling with fragile automation or scaling QA processes, I can help you build a durable quality strategy. Explore my Consulting Services or learn more about Fractional QA Leadership.