In today’s data-driven world, organizations succeed or fail based on the quality of their data. Our client, a leading data intelligence company, empowers enterprises with a platform for data and AI governance, turning raw data into a trusted, business-ready asset.
At the core of their platform is Data Quality & Observability (DQO), a powerful engine that ensures data reliability in real time. From monitoring and testing to delivering monthly product releases, DQO helps customers maintain confidence in their data while keeping pace with innovation. But with growth came new challenges, including tight release windows, the need for automation, and the pressure to keep customers’ trust uncompromised. To address this, Tech Vedika partnered with the client’s team to build a scalable, automation-first approach to testing.
Challenges
As DQO expanded across diverse data platforms, BI tools, and ETL technologies, the team faced hurdles that threatened speed and scalability:
- Shifting from manual-heavy testing to automation-first validation.
- Running full regression testing in just 2 days (down from 7) and bug fixes in 1 day (down from 3).
- Delivering a reusable test framework to scale validation across multiple releases.
- Maintaining flawless quality in customer environments while preventing production defects.
Technology/Tools Used:
- Pytest with Python – Ensuring faster, reliable, and scalable API test automation to boost product quality.
- Allure Reporting – Delivering rich, user-friendly test reports with actionable insights for quicker defect resolution.
- GitHub Actions – Enabling continuous integration and smooth deployment with automated, on-time test executions.
- Jenkins – Powering efficiency with automated nightly regression runs, ensuring uninterrupted release cycles.
- JAWS & NVDA – Guaranteeing inclusive digital experiences through comprehensive accessibility testing.
Solution
Working closely with the client’s team, Tech Vedika helped design and implement a robust, scalable test automation framework tailored to the unique demands of Data Quality & Observability:
- Automated regression suites safeguarding ~60% of product functionality, ensuring stability across frequent releases.
- Reusable, modular frameworks with 900+ tests, cutting repetitive effort and future-proofing validation.
- Parallelized test execution in Jenkins pipelines, shrinking regression cycles from 7 days to under 4 hours.
- In-sprint automation, collaborating with developers to validate new features on the fly, reducing rework and speeding up delivery.
- A shift towards software development engineering in test (SDET), leveraging deep product knowledge to design smarter tests and spot issues earlier.
- Customer-simulated environment testing, replicating real-world scenarios to boost reliability and proactively resolve client-facing issues.
The Impact
- 2x faster go-to-market — Meeting aggressive 2-day regression and 1-day bug-fix release goals.
- Improved reliability & customer trust – Critical workflows validated against customer-like environments.
- 60%+ automation coverage — Minimizing manual effort, enabling teams to focus on innovation.
- Sustainable efficiency — Reusable frameworks and parallel execution reduced testing effort dramatically.
By embedding automation, agility, and customer-first validation into the testing strategy, the initiative accelerated release readiness while reinforcing platform reliability. This strengthened the client’s position as a trusted leader in Data Governance and Observability, delivering what matters most: data customers can trust every time.