Pros: Good perks. Awesome company events. The business model is still good. Lots of big data use cases in important domains. Good enough infrastructure supports. Can potentially be a great place for a data engineer to learn and to grow.

Cons: I only focus on data engineering and advanced analytics, as I do feel I have the technical authority to comment. There are lots of tech debts. Quite a few managers at some very important positions are clueless about the future. They have been struggling to just prevent things from falling apart and to maintain the status quo. They are not good enough at telling the quality of data engineering work to reward the right people accordingly. This is the basic requirement for a healthy environment to make things improve over time, but it takes a lot to have it right. Engineers initiated data projects with good visions are almost nonexistent because there are not that many experienced and good data engineers who have stayed long enough to get to the bottom of the debt-infested systems. Even if they do, they are blocked from knowing the broader context due to heavy management involvement aiming to address the complicated coordination process. This leads to data systems that are not as simple/elegant as they can be, which reinforces more complicated management processes and the practice of micromanaging. And this is a vicious cycle. Suboptimal data systems may work okay when the company was at a smaller scale by resorting to more manual processes, but the same level of quality can easily push things beyond return at the current scale. This is yet another less recognized scaling problem specifically for data engineering. There have been major confusions in important data projects not resolved for a long time, and the managers just started working on something else. Over time, data engineers are increasingly shielded from the real problems but only exposed to the managers' limited and confused perceptions. As a consequence, some important but hard problems are left unsolved forever. And it will keep dragging down the quality of the entire system. This can be a major roadblock for new business partnerships and revenue expansion.

Pros: 1. 3 days a week in-office requirement. 2. Growing team in India. 3. Good facilities overall, including food, cab, and mobile facility.

Cons: 1. The tech stack is Scala and TypeScript, which is not great. Backend engineers also need to look at the FE and understand it. 2. Data science, QA, and the insurance product team exist in India as of now. In product teams, there's nothing much to explore from a tech perspective on the open-source side. You need to work on a predefined internal tech stack based on Scala/TypeScript with business logic. Not so great. 3. There are no internal mobility opportunities. 4. Not much exposure for junior engineers.

Pros: - Worked in a user-facing, big revenue team. - Full-stack project. - Great company culture.

Cons: - May not be a straightforward process for returning FT