Good name on CV; Learn basics of markets - Global Data Analyst bei Bloomberg: Mitarbeiterbewertung

2,0
19. Nov. 2019
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CEO-Befürwortung
Geschäftsprognose

Pros

- Young, fun workforce - Opportunity to learn how different market participants use Bloomberg and its data - Learn how to use the terminal - Potential to work on products that can be later rolled out to terminal users (although much of this work is tedious) - Decent starting salary

Kontras

- Work Split: There are 2 broad areas of work , operational and project work. At interview the managers will tell you that the rough split of the work is 50/50, whereas in reality it is closer to 80/20 in favour of manual, boring operational work. - Boring: This job was not engaging. Much of the work is comprised of reading documents and entering details into a screen, or checking the details entered by a different analyst are indeed correct and filling out a checklist. Aside from this, a huge amount of time is spent manning the Bloomberg Helpdesk, where you spend your day dealing with compaints or queries about the data from Bloomberg users - Project work: When you are not doing operational work, the "projects" that are undertaken are often little more than ad-hoc data validation/correction required by another department or business unit. As a "data analyst" you are often tasked with manual data checking or cleaning, which is dressed up as you playing a key role in a very valuable project that is vital for the business. Generally you end up doing tonnes of work that nobody else wanted to do, to unrealistic deadlines, while management with little knowledge of the product push you for deliverables. - Management: Far too often the managers at the firm are simply people who joined the firm at entry level and have stayed with the firm for a long time and gotten promoted due to loyalty. Frequently I felt that the managers' knowledge of the product and work that analysts do was lacking, and often placed too much emphasis on simple metrics of productivity (such as workitems completed, helpdesk tickets managed) and as a result neglected complex work and encouraged selective work to maximise metrics - Pay: The base salary is decent for a graduate (although working 8-6pm means that per hour its not too impressive). However, the bonus and pay progression are very bad. After my first year I received a 3% bonus and 3% pay rise, whilst my peers at other firms in the industry were receiving closer to 10% increases and bonuses. When I challenged management on this they couldn't justify the poor progression and tried to explain it away using excuses like budgets and market conditions. Further to this, if you move internally and receive a payrise, you do not receive the payrise until your next annual review is complete. This means that if you move to a more senior position 1 month after a review, you do not get paid more for another 11 months. Nobody at the firm was able to give me a reasonable answer as to why - Progression: There are very few opportunities to progress at the firm

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5,0
8. Juli 2026
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CEO-Befürwortung
Geschäftsprognose

Pros

good pay, great team, lots of experience gained

Kontras

mundane tasks sometimes, can be competitive

4,0
28. Juni 2026
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CEO-Befürwortung
Geschäftsprognose

Pros

Opportunities to do lots of work with data and finance to apply knowledge in both programming and Subject-Matter Expertise (SME). Excellent Work-Life Balance (WLB) and extremely welcoming culture. You can reach out to anyone for help or just to talk, and they will get back to you (although management does require more scheduling in advance). Generous compensation (good wage) and benefits, including housing for interns. If you heard the rumors that the Bloomberg Princeton office has a great Bloomberg Pantry (read: company-provided breakfast and lunch), the rumors are true.

Kontras

Not the place for those looking for cutting-edge AI. The company is not as fast with AI as the company prioritizes reliability and accuracy above all, and much of AI is not at an acceptable threshold for management to be willing to take that risk with financial data (at least in 2026). You may get a project to automate menial processes, which is really cool, but that tends to involve actually doing the menial processes, which feels unproductive. Princeton office is good but New York is considered preferable. Coworkers are not very reachable outside of work hours. Compensation is low in Data compared to Software Engineers.

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