Data Analyst

Cognizant Data Analyst is a Chennai-based analytics role with a strong GenAI focus. You will collect and clean complex enterprise data with Python, build reusable pipelines, and design generative AI workflows that automate text analysis, summarisation, and knowledge extraction for business teams.

What you'll do

• Collect, clean and structure enterprise data with Python for reporting, forecasting and insights.
• Design robust Python data pipelines and reusable code for ingestion, transformation and validation.
• Build generative AI workflows using LLMs for text analysis, summarisation and knowledge extraction.
• Develop MCP-based integrations that manage prompts, context windows and tool connections.
• Build LangGraph workflows and LangChain chains/agents that securely connect models to internal data.
• Analyse large structured and unstructured datasets, set up data quality checks and document solutions.

What they ask for

• Engineering or science graduates with strong Python
• Working knowledge of LLMs, prompt design and responsible AI practices
• Hands-on projects with LangChain, LangGraph or agent frameworks
• SQL and data-wrangling skills
• Clear communication with business stakeholders
• Comfortable with a hybrid schedule in Chennai

Good to have

• MCP exposure

Selection process

1. Resume shortlisting against Python and GenAI skills
2. Technical interview on Python, SQL and data analysis
3. GenAI deep-dive – LLM workflows, LangChain/LangGraph and a project walkthrough
4. HR discussion

Sign in or create a profile on the official portal, upload your resume with GitHub links to your Python and GenAI projects at the top, and submit.

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Put these words in your resume and practise these questions before the interview.

Technical

1. How do you handle missing values and clean structured data using Python pandas?
2. Can you explain how you would build a multi-step workflow using LangChain chains and LangGraph?
3. What is Model Context Protocol and how does it manage tool connections and context windows?

Scenario

1. What would you do if a Python data pipeline fails midway due to dirty or unstructured data from an enterprise source?
2. How would you approach designing a generative AI workflow to extract insights from a large unorganised text dataset?

HR

1. Can you tell me about a time you had to explain complex technical data insights to a business stakeholder?
2. Why do you want to work at Cognizant in a role combining data analysis with generative AI?

About Cognizant

1. How does Cognizant leverage generative AI solutions to drive enterprise transformation for its clients?

Practice questions written from the job listing, not this company's real interview questions.

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