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Job description
A Specialist Software Engineer - Informatica
Role Overview
We are seeking a Python AI Application Developer to design and develop intelligent applications that automate product manufacturing requirements validation and compliance checks. The role involves building AI-powered solutions that analyze engineering, manufacturing, and process data to ensure requirements consistency, completeness, and manufacturability.
Key Responsibilities
Understand user expectations and develop technical solutions and raises clarifications with stakeholders.
Respect all development practices and guidelines.
Ensure principles of security and architecture is maintained
Work effectively with other team members by sharing best practices.
Required Skills & Qualifications
Exp : 3+ yrs
- Informatica : Advanced Level
- Oracle SQL : Advanced Level
- Oracle PL/SQL : Advanced Level
- Unix commands / shell scripts : Advanced level
- Knowledge on Bigdata technologies. Control-M and JIRA - Added advantage
Knowledge or work experience in Agile methodology and Banking domain : Added advantage
Location: Bangalore
Most IT Jobs posts in Bangalore ask for 3–6 years of experience (41% of 3125 that say).
From live job posts on CoreHRx, updated 3 Nov 2026. Skill figures count posts that list their skills; salary figures only posts that show a salary.
The post states ₹8–11 LPA.
The post asks for 3 Yrs experience.
In Bangalore.
It is a full time job.
You apply on Arminus' website. CoreHRx takes you there after you sign in.
Interview preparation · 60 questions
This guide is for Indian job seekers preparing for SQL rounds in data analyst, data engineer, backend developer and database roles, from freshers to experienced professionals. It has 60 questions in three levels: easy covers joins, grouping and NULLs; medium covers window functions, CTEs, indexes and isolation levels; hard covers locking, MVCC, query tuning and classic puzzles such as login streaks and medians. Answers use ANSI SQL, with MySQL 8 and PostgreSQL notes where the two differ. Write each query against your own sample tables first, then compare your result with the sample answer.
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