Tiger Analytics Programmer Analyst Trainee salaries in India

Likes

Great Compensation. Vast number and variety of projects.

Dislikes

Terrible lead (he was a SIMP). Terrible manager (used to ping at 9 pm onwards and ask to complete immediate adhoc request within same day i.e. by 12 am or so ). Project allocation not in line with skills in my case. No work life balance as everything is urgent for managers. Adhoc tasks keep on barging in. After that these managers and leads snub your credits for all your hardwork which is very demotivating.

Likes

Great work in killing the enthusiasm to learn. Great mindset of management, they think data engineers can never learn Machine learning. They ask data engineer do you know how to write a code. In their opinion Data Engineer doesn't know how to write a code.

Dislikes

Data Engineer are underdogs in every aspect like salary, they way you'll be treated. Data Science > Data Engineer. Doesn't matter you willingness to learn or how skilled you are. No chance for carrier change.

Likes

...ers collaboration and learning from people at different levels of their careers. Their pay structure is excellent and the leadership is extremely considerate of employees' welfare.

Dislikes

Tiger needs more opportunities for in-house learnings and certifications. They do however reimburse limited certification programs from external vendors.

Likes

The compensation was great.

Dislikes

Most of the things apart from compensation were pathetic. 1.) Learning curve was flat, I didn't learn thing here. The onboarding process for the project was pretty much non existent. The project I was a part of was a maintenance project so nothing to learn. 2.) The team I was a part off had some serious internal politics something I have not experienced till date, stealing credits, sidelining you from project discussion are some of the things I have observed. 3.) Micromanagement and too many unnecessary meetings was another core theme. 4.) Work life balance is non existent, and having more than 2 managers often caused more harm than any good.