Data Analyst

Cognizant Data Analyst 2026 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; MCP exposure is a plus.
• SQL and data-wrangling skills, plus clear communication with business stakeholders.
• Comfortable with a hybrid schedule in Chennai.

Good to have

• Model Context Protocol 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

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Technical

1. How do you handle missing values and data cleaning in large datasets using pandas?
2. Can you explain how you set up memory and state management in a LangChain or LangGraph workflow?
3. What are window functions in SQL and how do you use them for data analysis?

Scenario

1. What would you do if a generative AI workflow starts returning hallucinated or inconsistent outputs when querying internal data sources?
2. How would you approach designing a data pipeline for an unstructured dataset that frequently changes format?

HR

1. Why do you want to transition into a generative AI-focused analytics role at Cognizant?
2. How do you prioritize your tasks when managing multiple data pipeline development requests from stakeholders?

About Cognizant

1. How does Cognizant leverage generative AI solutions to deliver enterprise value to its global clients?

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

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