AWS Data Engineer
Infosys
Pune Division, Maharashtra, India1h agovia LinkedIn
This job at a glance
- Who can apply
- Freshers
- Must know
- AWSETLSQLSparkTroubleshootingEHR / EMR
- Nice to have
- GitCI/CDAgilePython
- Work
- On-site in Pune Division
- Pay
- Not stated by the employer.
- Listed
- 12 Nov 2026 · still open
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About the Role
- Design, develop, and maintain scalable data pipelines on AWS.
- Develop ETL/ELT workflows for data ingestion, transformation, and processing.
- Build data processing solutions using PySpark and SQL.
- Perform data transformations, validations, and data quality checks.
- Optimize data pipelines and SQL queries for performance and scalability.
- Work with different data sources and integrate data into cloud-based data platforms.
- Troubleshoot data pipeline failures and resolve data-related issues.
- Collaborate with business, analytics, and engineering teams to understand data requirements.
- Ability to independently understand requirements and deliver data engineering solutions.
Key Responsibilities
- Design, develop, and maintain scalable data pipelines on AWS.
- Develop ETL/ELT workflows for data ingestion, transformation, and processing.
- Build data processing solutions using PySpark and SQL.
- Perform data transformations, validations, and data quality checks.
- Optimize data pipelines and SQL queries for performance and scalability.
- Work with different data sources and integrate data into cloud-based data platforms.
- Troubleshoot data pipeline failures and resolve data-related issues.
- Collaborate with business, analytics, and engineering teams to understand data requirements.
- Ability to independently understand requirements and deliver data engineering solutions.
Requirements & Tech Stack
- Strong hands-on experience with AWS Data Engineering services such as S3, Glue, Lambda, EMR, Redshift, Athena, and IAM.
- Strong programming experience in PySpark.
- Excellent knowledge of SQL, including complex queries and data transformations.
- Hands-on experience in developing ETL/ELT pipelines.
- Good understanding of data warehousing, data lakes, and data modeling concepts.
- Experience handling large-scale datasets and optimizing distributed data processing workloads.
- Familiarity with Git, CI/CD, orchestration tools, and Agile development practices will be an advantage.
- Exposure to cloud migration or modernization projects would be an added advantage.
Qualifications
- Good analytical and problem-solving skills.
- Strong communication and collaboration skills.
- Knowledge of Python is preferred.