New opportunity

Data Engineering(ETL+Python)

Infosys Limited · Hyderabad, India

About this role

The Data Engineering (ETL+Python) role at Infosys Limited focuses on building robust data pipelines and enabling AI-driven data architectures. The successful candidate will bridge traditional data engineering with modern AI requirements to support operational intelligence and agentic workflows. Responsibilities include: Data Engineering: Build and maintain operational data pipelines, develop ETL/ELT solutions, ingest data from enterprise systems, and design scalable data models. AI Data Enablement: Build vector databases and semantic search capabilities, develop RAG pipelines, create operational knowledge repositories, and support AI agent context engineering. Analytics & Insights: Perform root cause analysis, create automation outcome dashboards, build operational intelligence models, and develop KPI measurement frameworks. Data Governance: Ensure data quality and lineage, implement data security controls, and support compliance and audit requirements. Required Qualifications: Bachelor of Engineering, BTech, MTech, MCA, or MSc. Preferred Experience: 5+ years of data engineering experience, AI/ML project experience, experience building RAG solutions, and familiarity with data observability tools. Technical Skills: Proficiency in Python, SQL, Data Modeling, Vector Databases, LangChain, LangGraph, Knowledge Graphs, Semantic Search, AWS Data Services (S3, Glue), Power BI, and Tableau.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PythonSQLData ModelingVector DatabasesLangChainLangGraphKnowledge GraphsSemantic SearchAWS Data ServicesS3GluePower BITableauETLELT

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Building AI-Ready Data Pipelines
The role requires bridging traditional ETL/ELT data engineering with modern AI requirements. You must demonstrate the ability to ingest data from enterprise systems while simultaneously structuring it for RAG pipelines and vector databases to support agentic workflows.
AI Data Enablement and Context Engineering
A core priority is the development of semantic search capabilities and knowledge repositories. You will be expected to use tools like LangChain and LangGraph to engineer the context necessary for AI agents to function effectively within operational environments.
Operational Intelligence and KPI Frameworks
Beyond data movement, you are responsible for building operational intelligence models and dashboards. This requires translating raw data into actionable insights and KPI frameworks that measure the outcomes of automation and AI initiatives.
Data Governance and Observability
Maintaining data quality, lineage, and security is critical. You must implement robust data observability practices to ensure that the data feeding AI models remains compliant, secure, and reliable throughout its lifecycle.

Likely challenges to think through

Balancing Traditional ETL with Generative AI
The primary challenge is the shift from standard batch processing to real-time, context-aware data delivery for AI. Prepare by articulating how you handle the trade-offs between data latency and the high-quality, structured requirements of vector databases.
Managing Data Quality in Unstructured AI Contexts
Ensuring data lineage and quality becomes significantly more complex when dealing with semantic search and knowledge graphs. Prepare to discuss how you maintain governance standards when data is transformed for non-traditional AI consumption.
Adapting to Agentic Workflow Requirements
AI agent context engineering is a specialized task. You may face difficulty in defining the right granularity of data for agentic retrieval. Prepare by reviewing how you have previously optimized data retrieval patterns for LLM-based applications.

Practice your answers

Technical practice

Designing Scalable ETL/ELT Pipelines

What to coverExplain your approach to designing data pipelines that handle high-volume ingestion from enterprise systems. Discuss how you balance batch processing with real-time requirements and ensure scalability using AWS services like Glue and S3.

Implementing RAG Pipelines

What to coverDescribe the architecture of a Retrieval-Augmented Generation (RAG) pipeline you have built. Focus on how you handle document chunking, embedding generation, and the integration of vector databases to improve AI model context.

Vector Databases and Semantic Search

What to coverCompare different vector database solutions and explain how you optimize them for semantic search performance. Discuss the trade-offs between various indexing strategies and how they impact retrieval accuracy.

Data Modeling for AI Context

What to coverHow does data modeling for AI agent context differ from traditional relational modeling? Explain how you structure data to support Knowledge Graphs and ensure it remains queryable for AI-driven workflows.

Data Governance and Lineage

What to coverDescribe your methodology for maintaining data lineage and quality in complex pipelines. Suggestion: Focus on how you implement automated checks and security controls to meet compliance requirements during the ETL process.

Operational Intelligence and KPI Frameworks

What to coverHow do you translate raw pipeline logs into actionable operational intelligence? Discuss your experience in building dashboards using Power BI or Tableau to track KPIs and perform root cause analysis on pipeline failures.

LangChain and LangGraph Application

What to coverProvide an example of how you have used LangChain or LangGraph to manage state or multi-step reasoning in an AI agent. Explain how these tools simplify the orchestration of complex agentic workflows.

Data Observability

What to coverWhat metrics and tools do you use to ensure data observability in production environments? Suggestion: Discuss how you proactively detect data quality issues or pipeline bottlenecks before they impact downstream AI models.

Behavioural practice

Handling Ambiguous AI Requirements

What to coverDescribe a situation where you had to build a data solution for an AI project with evolving or unclear requirements. Explain how you managed stakeholder expectations and ensured the architecture remained flexible.

Resolving Critical Data Pipeline Failures

What to coverTell me about a time you had to perform a root cause analysis on a failed production data pipeline. Detail the steps you took to identify the issue, mitigate the impact, and prevent recurrence.

Collaborating Across Teams

What to coverGive an example of a project where you had to bridge the gap between traditional data engineering and AI/ML teams. How did you ensure the data provided met the specific needs of the AI models?

Prioritizing Data Security and Compliance

What to coverDescribe a scenario where you had to implement security controls or audit requirements in a data pipeline. How did you balance the need for data accessibility with the necessity of strict governance?

Sharpen your knowledge

Topics to prepare

RAG Pipeline Architecture
Review the conceptual flow of Retrieval-Augmented Generation, specifically how data is ingested, chunked, and retrieved to provide context for AI agents. This is essential for the role's requirement to build RAG pipelines and support agentic workflows.
Data Governance and Lineage
Study the principles of data lineage, quality assurance, and security controls within enterprise data pipelines. This is directly relevant to the responsibility of ensuring data integrity and meeting compliance/audit requirements.
Operational Intelligence Modeling
Understand how to translate raw pipeline metrics into actionable operational intelligence and KPI frameworks. This aligns with the requirement to perform root cause analysis and build automation outcome dashboards.

Skills to revise

Python for ETL and AI
Practice writing efficient Python scripts for data transformation and integrating with AI frameworks like LangChain or LangGraph. This is a primary technical requirement for both traditional ETL and modern AI data enablement.
Vector Database Implementation
Gain hands-on experience setting up and querying vector databases to support semantic search capabilities. This is a core requirement for the AI Data Enablement portion of the job description.
AWS Data Services (S3 and Glue)
Develop proficiency in using AWS Glue for ETL job orchestration and S3 for scalable data storage. These are the specific cloud tools identified for building and maintaining operational data pipelines.
Data Modeling
Practice designing scalable schemas that support both structured operational data and unstructured data for AI context engineering. This is fundamental to the role's data engineering responsibilities.

Your preparation checklist

Prepare a RAG Pipeline Project Walkthrough
Develop a concise technical summary of a project where you built a Retrieval-Augmented Generation (RAG) pipeline. Be prepared to explain your choice of vector database, how you implemented semantic search, and how you integrated LangChain or LangGraph to manage agentic context.
Demonstrate ETL and Data Modeling Proficiency
Review your experience with building scalable data models and ETL/ELT workflows. Prepare to discuss specific challenges you solved regarding data ingestion from enterprise systems and how you ensured data quality and lineage in your previous pipelines.
Map AWS Data Services to Operational Needs
Prepare examples of how you have utilized AWS S3 and AWS Glue to support data engineering tasks. Focus on how these services were configured to maintain robust, production-grade data pipelines.
Articulate Data Governance and Observability Strategies
Be ready to explain your approach to implementing data security controls and compliance audits. Discuss your familiarity with data observability tools and how you use them to monitor pipeline health and perform root cause analysis.
Formulate Strategic Questions for the Interviewer
Prepare thoughtful questions such as: How does the team currently balance traditional ETL maintenance with the development of new AI-driven agentic workflows? What are the primary data quality challenges the team faces when building operational knowledge repositories?

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