AWS Data Engineer
Infosys
Bengaluru East, Karnataka, India2h agovia LinkedIn
This job at a glance
- Who can apply
- Freshers
- Must know
- AWSSparkETLSQLTerraformCI/CD
- Work
- On-site in Bengaluru East
- Pay
- Not stated by the employer.
- Skill demand
- SQL23%ETL3%of 30 similar fresher jobs posted in India this week
- Listed
- 9 Nov 2026 · still open
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About the Role
Design and develop scalable AWS Data Engineering solutions and cloud-native data platforms.
Key Responsibilities
- Design and develop scalable AWS Data Engineering solutions and cloud-native data platforms.
- Build batch and real-time data pipelines using AWS Glue, PySpark, Databricks, S3, Kinesis, Redshift and Kafka/MSK.
- Develop and optimize Databricks, Delta Lake and Spark workloads for large-scale data processing with PySpark, Databricks transformations.
- Implement streaming solutions using Amazon Kinesis Data Streams, Firehose and Analytics for near real-time data ingestion and processing.
- Handle high-volume (TB/PB scale) data pipelines with a focus on performance, scalability and cost optimization.
- Develop complex SQL transformations, data models and analytics-ready datasets.
- Implement CI/CD, Infrastructure as Code (Terraform/CloudFormation) and DevOps best practices.
- Ensure data quality, governance, security, monitoring and production support.
- Collaborate with architects, stakeholders and cross-functional teams in Agile delivery environments.
- Troubleshoot and optimize end-to-end AWS data platforms, streaming pipelines and cloud infrastructure.
Requirements & Tech Stack
- Strong hands-on AWS Data Engineering experience across large-scale cloud data platforms.
- Expertise in AWS S3, AWS Glue, AWS Lambda, AWS Redshift, AWS Lake Formation, AWS CloudWatch and AWS IAM.
- Strong experience in real-time and streaming data processing using Amazon Kinesis Data Streams, Kinesis Firehose, Kinesis Analytics, Amazon Kafka or equivalent streaming technologies.
- Hands-on coding proficiency in Python, PySpark and SQL with strong optimization skills.
- Experience building high-volume, low-latency data ingestion and transformation pipelines.
- Strong experience in Databricks, Delta Lake, Structured Streaming and Spark Performance Tuning.
- Experience handling TB/PB-scale datasets, partitioning strategies, file optimization, schema evolution and data lifecycle management.
- Strong understanding of Data Lake, Lakehouse and Modern Data Platform architectures.
- Practical data modeling experience for analytical, operational and reporting workloads.
- Experience implementing CI/CD pipelines using AWS CodePipeline, CodeBuild, CodeDeploy, GitHub Actions or Jenkins.
- Strong troubleshooting skills for production issues, performance bottlenecks and cost optimization.
- Excellent client communication, stakeholder management and problem-solving skills.