AWS Data Engineer - BNG/CHN/HYD-Permanent Role with MNC

About the Role:

We are looking for a highly skilled and motivated Senior Data Engineer to lead the design and development of scalable, secure, and high-performance data pipelines and lakehouse architectures on AWS. You will play a critical role in building and optimizing our data infrastructure, enabling real-time and batch analytics across structured and semi-structured data sources.

Key Responsibilities:

• Design, develop, and maintain scalable ETL/ELT pipelines using AWS Glue, Glue Studio, and Glue Workflows
• Build and manage data lakes and lakehouses using S3, Lake Formation, and Glue Data Catalog
• Enable interactive and federated querying using Athena, Redshift Spectrum, and Presto
• Ingest and process streaming data using Amazon Kinesis (Data Streams, Firehose)
• Integrate and transform data from SQL and NoSQL sources (e.g., RDS, DynamoDB, MongoDB)
• Implement data governance, lineage, and access control using Lake Formation, IAM, and resource policies
• Optimize performance, cost, scalability, and storage efficiency across pipelines, catalog layers, and compute frameworks
• Implement data quality checks, validation frameworks, and observability mechanisms to ensure reliable, trustworthy datasets
• Work with data scientists and analytics teams to deliver curated, clean, and production-ready datasets for ML, BI, and reporting
• Ensure compliance with security, encryption, auditing, and regulatory standards
• Develop architectural diagrams, data models, transformation logic, and operational documentation
• Collaborate in Agile/Scrum processes involving sprint planning, estimation, and retrospectives
• Troubleshoot production issues, perform root cause analysis, and drive continuous improvement of data systems

Required Skills & Qualifications:

Category

Requirements

ETL & Data Pipelines

Strong experience with AWS Glue, PySpark, Athena, and Glue Data Catalog

Data Storage

Proficient with Amazon S3, Redshift Spectrum, RDS, DynamoDB, and Lake Formation

Streaming

Hands-on with Amazon Kinesis (Data Streams, Firehose)

Data Modeling

Strong knowledge of:

• SQL & NoSQL design
• Partitioning strategies
• Schema evolution and versioning
• Performance tuning and query optimization
• Data normalization vs. denormalization strategies