Situation:

I joined a major Russian bank's new innovation department focused on developing smart devices and voice assistants, essentially a 900-person startup operating within a regulated banking environment. The department was tasked with creating a competitive product to rival Amazon Alexa, targeting both consumer markets and specialized hotel solutions. When I arrived, testing was entirely fragmented—developers performed ad-hoc testing based on individual preferences, with no standardized approach or reusable assets. Manual QA teams were overwhelmed by growing regression needs, with each incremental change requiring significant validation effort. The initiative was barely a year old with no production release yet, but faced aggressive go-to-market timelines and high executive visibility as a flagship innovation project for the bank.

Obstacle:

I faced multilayered challenges requiring both technical and organizational solutions. The environment combined startup-level urgency with enterprise banking constraints—every access request between services or environments required weeks of compliance reviews and approvals. There was no existing test automation team or infrastructure, requiring me to define, hire, and build this function from scratch while simultaneously defining the architecture. The solution needed to cover disparate technologies including web applications, Android devices, voice assistants with NLP capabilities, and complex backend integrations. Additionally, I needed to build trust with development teams who were accustomed to their own testing approaches but weren't yet familiar with centralized automation strategies. All of this occurred under relentless deadline pressure as the business pushed to capture market share before competitors.

Action:

I developed and executed a comprehensive test automation strategy that addressed both technical and organizational challenges:

• Designed a specialized multi-framework architecture with purpose-built solutions for each testing domain:

  • Java-based framework for web UI testing with optimized Selenium implementation

  • Dedicated API/integration framework with configurable mock server using JSON schema

  • Kotlin-based Android UI E2E testing framework aligned with native app development

• Implemented scalable execution architectures optimized for parallel testing to maximize throughput despite limited environment resources

• Built and led a 14-person automation team structured to balance specialization with cross-training—each engineer owned a specific domain area (web, Android, API integration, NLP) while contributing to core framework development

• Implemented a strategic rollout approach, working with one development team at a time, starting with web applications as the foundation

• Positioned the automation initiative as a support function rather than an oversight mechanism, emphasizing our role in "taking headaches away" from development teams

• Established clear communication channels and synchronization points with each development team to maintain visibility and alignment

• Prioritized automation efforts based on business risk and release schedule requirements, focusing on high-value regression scenarios first

Result:

The framework we built transformed testing capabilities across the entire smart devices division, delivering measurable business impact. We achieved comprehensive test coverage across multiple platforms: 100% automation of voice assistant testing (1,670+ test scenarios), complete automation of web-based testing (1,200+ scenarios across six products), 60% coverage of integration testing for API/message bus compatibility, and 80% automation of Android UI testing (190 scenarios). The most dramatic efficiency gain came in regression testing, where we reduced cycle time from two full working days to just 90 minutes—an 95% reduction that created critical breathing room in the release schedule. Our parallel execution architecture supported 40 concurrent test threads, maximizing throughput within environment constraints. The automation framework became fundamental to maintaining release cadence and enabling monthly stakeholder demonstrations, with executives amazed by areas where automation was complete while pushing for acceleration in areas still in progress. Most importantly, the framework consistently caught critical issues before release—from broken voice assistant skills to mid-flow Android UI failures and backward compatibility breaks—proving its value in a startup environment where rapid changes often introduced unexpected defects across the technology stack.