Data Engineering (ETL + Python), Infosys

Data Engineer
India, Hyderabad
October 14, 2026
Full Time

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Job Description

Infosys is hiring a data engineering professional in Hyderabad to develop scalable data pipelines, ETL solutions, AI-enabled data capabilities, and operational analytics. The role involves building RAG pipelines, vector databases, semantic search solutions, knowledge repositories, and KPI dashboards. Candidates should bring strong Python, SQL, cloud data services, data modelling, governance, and AI/ML project experience. 

Qualification: Bachelor of Engineering 

Experience: 5–9 years

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Responsibilities

  • Construct and manage operational data pipelines utilizing scalable ETL and ELT systems for enterprises.
  • Create vector databases, semantic search, and RAG pipelines for advanced AI systems.
  • Build knowledge bases and enable AI agent context engineering along with operational needs.
  • Root cause analysis and dashboard creation will aid in operational intelligence and effectiveness.
  • Ensure quality, provenance, security, compliance, and auditing in enterprise data management scenarios.

Requirements

  • Must have at least 5–9 years of experience in data engineering or any other technical field.
  • Should be well versed with Python, SQL, ETL concepts, and scalable data modelling practices.
  • Must know vector databases, LangChain, LangGraph, knowledge graphs, and semantic search.
  • Familiar with AWS data services like S3 and Glue.
  • Hands-on experience with AI/ML implementations and building robust RAG solutions.

Preferred Qualifications

  • Hands-on experience working on enterprise data pipeline architecture and developing ETL/ELT processes.
  • Hands-on experience in designing and deploying RAG solutions along with AI-driven data platforms.
  • Knowledge of data observability tools and techniques that help in performing efficient data operations and monitoring.
  • Practical knowledge about the use of Power BI/Tableau in analytics and KPIs.
  • Thorough knowledge about data governance, data quality, data lineage, data security, data compliance and enterprise data management.

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