Data Analyst - werkgeversreview Data Analyst bij Techne AI

Chill flexible good stepping stone

Not much scope to learn or grow

They pay salary on time.

I had a disappointing experience during my internship at this company. I was part of the testing team, where the QA lead demonstrated limited knowledge of automation testing. In the second week of my internship, she instructed me to automate 60–70 test cases that I had previously written, without providing any guidance or support. Despite putting in extra effort—sometimes even skipping lunch—I couldn't complete the task due to a lack of direction, and it was eventually canceled. In October 2023, during the third week, the manager called me and another intern to assign us the task of building an API automation framework within 2–3 days. We had no prior experience with API automation and requested one week to learn the basics, but our request was denied, and the task was again canceled. For context, most online API automation courses require at least 15–30 days to complete. There was a constant expectation to meet unrealistic deadlines. If tasks weren’t completed in time, I often had to work on Saturdays and sometimes even Sundays. At the end of February 2024, I was asked to execute 600 test cases in a single day, which was not feasible. I had to work through the weekend to complete it. The QA lead casually mentioned that a tester should be able to execute 200–250 test cases per day, but not all test cases are straightforward. Many of mine included 4–5 negative scenarios each. Although my daily count averaged 120–130 test cases, factoring in the complexity, I effectively covered around 520 test conditions per day. Unfortunately, negative testing wasn’t considered in the overall performance evaluation. Later, I was assigned automation tasks for another project, and this time a senior tester walked me through the process. I successfully automated a test case in six hours, but the expected time was only 2.5 hours. The framework—Selenium with Java—was new to me, and it took time to understand the structure. The QA lead questioned my performance without considering these learning curves. At that early stage, I didn’t know how to explain the challenges I faced. To draw a comparison: if a driver used to a Suzuki Swift suddenly switches to a Mercedes, even with driving skills, they need time to adjust. Important aspects of automation like locating XPaths and handling exceptions were not factored into time estimates. These tasks were expected to be resolved during personal time. Similarly, data preparation, which is a critical part of the Software Testing Life Cycle (STLC), was never included in formal task planning. The company culture also felt tense. Whenever the manager was around, employees would abruptly stop laughing or socializing out of fear. In one instance, he told the QA team he would "stretch them out," meaning they were expected to learn new skills on their own time. The test case format used in the company lacks essential features like attachment columns or data input sections for execution evidence. I suggested improvements based on educational content from industry experts like Pavan Kumar, but there was no interest in updating the format. Interns and team members were often expected to help with other QA projects even if their own tasks were unfinished. Unlike other companies, where professionals are assigned to one project for years, here, multitasking across unrelated projects was mandatory. National holidays were not observed, and employees were expected to work through them. Even code merges, which should be done through GitHub, were handled manually by sharing code via Microsoft Teams. Lastly, the approach to regression testing was flawed. We were instructed to execute all test cases rather than focusing on those affected by new changes or bug fixes. Although one day of work-from-home per week was allowed, it was restricted to mid-week—Mondays and Fridays were off-limits.