Tiger Analytics Sde1 salaries in India

Likes

In current situation nothing good.

Dislikes

No learning and development planning. Higher management is just seating ideal. Tiger is still needsot of improvement in many areas such as job security, pay structure, work life balance. And this issue is across all the projects and that I can say because I am working here from last 5 years and due to which they made me to go and serch out for job.

Likes

The thing which I like about tiger is the company is providing work from home and decent amount of salary.

Dislikes

I have joined the organisation expecting great learning scope but it didn't work well, like other service based companies work differs from client to client and should be lucky enough to get a project where learning scope would be high.

Likes

MNC company working with emerging technologies like data science, data analytics and AI Not much work pressure, you just need to perform the assigned tasks. Decent management

Dislikes

...ills. Colleagues are not supportive and most of the times you are on your own. Company is not in the league of their competitors in terms of work or salary even though they are in the market for a long time.

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

Good work life balance Provides flexibility of wfh On time salary

Dislikes

Appraisal process, multiple review cycles in a year.(Compensation revised once per year). Expectations are too much. SDE and above are expected to work in their project, other firm initiatives, take interviews, Mentor atleast 5 people, provide review of each mentee every quarter. Lack of good projects on Data Engineering currently(2022). Most projects are of short duration.