Live opening · Posted 1 day ago

AWS Glue, Python

Infosys · Bengaluru East, Karnataka, India (On-site)
Linkedin No
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At a glance

The key details from the original listing.

Posted 1 day ago
CompanyInfosys
LocationBengaluru East, Karnataka, India (On-site)
Work modeNo
SourceLinkedin
Listed1 day ago

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About the role

Description supplied by the original job listing.

Primary skills :AWS Glue-Technology->Cloud Platform->Amazon Webservices DevOps,Technology->Cloud Platform->AWS Data Analytics->AWS Glue DataBrew,Technology->OpenSystem->Python - OpenSystem
Key Responsibilities:
Design, build, and maintain ETL/ELT pipelines using AWS Glue and Python for batch and incremental data processing.
Develop and optimize Glue Jobs (PySpark/Python) including job parameters, bookmarks, retries, and performance tuning.
Implement data ingestion, transformation, and validation logic to ensure accuracy, completeness, and consistency of datasets.
Integrate pipelines with AWS services (e.g., S3, IAM, CloudWatch) to enable secure, observable, and scalable workflows.
Troubleshoot job failures, analyze logs/metrics, and implement fixes to improve stability and runtime efficiency.
Collaborate with cross-functional teams to gather requirements, define data mappings, and deliver well-documented solutions.
Follow engineering best practices including code reviews, version control, and reusable modular coding patterns. Minimum Qualifications:
Bachelor’s degree or equivalent (e.g., BE/BTech/MSc/MCA/MTech).
3–5 years of experience in data engineering, ETL development, or data integration roles.
Strong hands-on experience with AWS Glue and Python for building production-grade data pipelines.
Working knowledge of core AWS concepts including security basics (IAM), storage patterns, and monitoring.
Ability to debug data pipeline issues and deliver reliable solutions with clear documentation. Preferred Qualifications:
Experience with PySpark and distributed data processing patterns within AWS Glue.
Strong SQL skills and experience working with structured/semi-structured datasets (CSV/JSON/Parquet).
Exposure to orchestration and scheduling patterns for ETL workflows and dependency management.
Familiarity with data quality checks, schema evolution handling, and building resilient pipelines.
Experience collaborating in Agile teams and contributing to CI/CD or automated deployment practices for data jobs. Good to have skills: PySpark, Amazon S3, AWS IAM, Amazon CloudWatch, SQL

Work arrangement
No

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