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

Robust knowledge of

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

📌 AWS Data Engineer - BNG/CHN/HYD-Permanent Role with MNC (Bengaluru)
🏢 MNC Group
📍 Bengaluru