Data Engineer – AWS Data Platform

Job Summary

We are looking for a highly skilled and motivated data engineer with strong expertise in AWS data services to join our data platform team. The ideal candidate will have hands-on experience designing scalable data pipelines, workflow orchestration frameworks, and large-scale data migration solutions .

This role will be responsible for building robust cloud-native data engineering solutions on AWS , migrating datasets from legacy systems and data warehouses, and ensuring secure and efficient data processing pipelines across distributed environments.

Key Responsibilities

AWS Data Pipeline Development

- Design and implement scalable ETL/ELT data pipelines using AWS Glue, AWS Lambda, and AWS S3 .
- Build and maintain high-performance data ingestion frameworks for processing large-scale datasets.
- Implement data pipelines for data warehousing and analytics platforms such as AWS Redshift .
- Optimize storage and querying strategies using AWS S3 data lakes .

Data Workflow Orchestration

- Develop and maintain data workflow orchestration frameworks using tools such as Apache Airflow or AWS Step Functions .
- Automate complex workflows including data ingestion, transformation, validation, and loading processes .
- Build reusable and configurable workflows to support multiple data processing use cases.

Data Migration & Integration

- Lead data migrations from legacy data warehouse technologies to modern AWS data platforms.
- Perform data migration from RDBMS systems (e.g., MySQL, SQL Server, Oracle) to AWS S3 or AWS Redshift .
- Design scalable migration frameworks for large datasets with minimal downtime.
- Integrate data sources from enterprise applications and external systems.

Data Security & Governance

- Implement secure data pipelines using AWS security best practices .
- Manage access control and data governance using AWS IAM and Lake Formation .
- Ensure data encryption, access management,



and compliance across all data platforms.

Performance Optimization & Monitoring

- Monitor data pipelines and troubleshoot performance issues.
- Optimize ETL workflows for scalability, reliability, and cost efficiency.
- Implement logging, monitoring, and alerting mechanisms for data pipelines.

Required Skills & Qualifications (Must Have)

- 5+ years of experience in Data Engineering or Data Platform development
- Strong hands-on experience with:
- AWS
- AWS Glue
- AWS S3
- AWS Lambda
- Experience with Data Workflow Orchestration tools such as Apache Airflow or AWS Step Functions
- Experience performing data migrations from other data warehouse technologies
- Experience performing data migrations from RDBMS systems to AWS S3 or AWS Redshift
- Strong expertise in Python and SQL for building scalable data pipelines
- Solid understanding of ETL/ELT concepts, data partitioning, and distributed data processing
- Experience working with version control systems such as GitLab or Bitbucket
- Strong debugging, analytical thinking, and problem-solving skills
- Basic understanding of Object-Oriented Programming concepts

Industry Knowledge & Experience

- Experience building cloud-native data engineering solutions on AWS
- Experience with data warehouse architectures and large-scale analytics platforms
- Hands-on experience with data extraction, transformation, and migration frameworks
- Experience working in high-volume data environments such as FinTech, analytics platforms, or enterprise data systems

Good to Have Skills

- IBM Cognos
- AWS Athena
- AWS Lake Formation
- AWS Redshift
- AWS Glue Data Catalog
- AWS SageMaker
- AWS IAM

Soft Skills

- Strong communication skills to present technical solutions and recommendations to stakeholders
- Ability to work cross-functionally in a quick-paced and evolving environment
- Detail-oriented with a proactive approach to identifying and solving data platform challenges
- Ability to collaborate effectively with data scientists, analysts, and platform engineering teams

📌 Senior Data Engineer (Noida)
🏢 Trantor
📍 Noida