Want to be a co-founder at a Rocket Internet startup? Hereโs one way
Rocket Internet, the German startup accelerator, is well known in tech circles for its strategy to replicate successful models of web businesses in markets that it believes are underserved but hold potential. While many criticize it for lacking innovation, its core belief is that successful startups are defined by execution, and not primarily by having a great idea. Central to this strategy is its stated policy of hiring executives with MBA degrees and prior experience in management consulting to lead its country-specific teams.
However, not all executives are born equal. For Muneeb Maayr, co-founder of ecommerce marketplace Daraz.pk, the journey of being chosen to lead a Rocket Internet venture was markedly different from that of his peers. (Disclosure: The author is a former PR manager for Daraz). Maayr does not have an MBA degree, or prior experience in management consulting. What he did bring to the table was a track record of successfully scaling companies, a willingness to get his hands dirty, and a dogged determination to never quit.
One could say that Maayrโs grit was instilled in him from a very young age. Despite being born into an โaffluentโ Pakistani family, his parents separated, and there were financial hardships.
Nevertheless, Maayr was determined to graduate from a top university in the US, and work towards landing a high-paying job. When he embarked upon his journey, his family handed him US$3,000 in cash and told him to find his way. That was all they could afford at the time.
โI knew I had to work, both to pay my way through college, as well as build up my resume to get a job after graduating,โ Maayr tells Tech in Asia. โI worked odd jobs during semester time, and did summer internships to appeal to prospective employers. I was fervent in my desire to land interviews at consulting firms or investment banks.โ
A dream quickly turned sour
Despite changing colleges three times, Maayrโs perseverance paid off. He graduated from the University of Virginia and landed a job at the former Wall Street investment bank Bear Stearns. Unfortunately, his time at the firm also coincided with the start of the US financial crisis. Bear Stearns was severely affected and went on to become one of the first โfailuresโ of the crisis, before eventually being liquidated and sold off to JPMorgan Chase. Maayr โ like thousands of other employees in the financial services sector โ found himself back in the job market.
It certainly wasnโt easy. Hiring during those times was almost non-existent, with firms scrambling to cover losses and downsize staff in an effort to cut costs. Maayr โ who didnโt have a US passport โ needed an employer to sponsor his work permit if he wished to stay. In a last ditch effort, he found himself back at his college town and met with a company called SNL Financial.

SNL Financial had a smart business model which was gaining traction during the time of the financial crisis. Essentially, it would โoutsourceโ analyst-level jobs in investment banks, venture capital firms, and private equity companies to qualified individuals in India. Due to wage disparities, firms in the US would be able to get the same work at a fraction of the salary they would normally pay their staff. This was particularly pertinent during the recession โ when firms were still expected to close deals, but operate at a fraction of the cost they were used to earlier.
Maayr says that the idea of outsourcing financial analyst jobs to other parts of the world had come to him much before he met with SNL Financial. He recognized that these jobs were essentially to pore over pages of financial data released by listed companies in the West and provide relevant numbers for managers to utilize in forecasting. A qualified MBA in his home country of Pakistan could do this work and relay it back instantly to the contracting firm in the US. All at a much reduced price.
See: This entrepreneur dropped out of high school, discovered engineering, and built a $1 million startup
Opportunity where least expected
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