10 plus years of experience in Data Engineering with solid experience in design, development and implementation of end-to-end data ingestion and data processing system in AWS platform.

2-3 years of experience in AWS Glue, Lambda, Appflow, EventBridge, Python, PySpark, Lake House, S3, Redshift, Postgres, API Gateway, CloudFormation, Kinesis, Athena, KMS, IAM.

Experience in modern data architecture, Lake House, Enterprise Data Lake, Data Warehouse, API interfaces, solution patterns, standards and optimizing data ingestion.

Experience in build of data pipelines from source systems like SAP Concur, Veeva Vault, Azure Cost, various social media platforms or similar source systems.

Expertise in analyzing source data and designing a robust and scalable data ingestion framework and pipelines adhering to client Enterprise Data Architecture guidelines.

Proficient in design and development of solutions for real-time (or near real time) stream data processing as well as batch processing on the AWS platform.

Work closely with business analysts, data architects, data engineers, and data analysts to ensure that the data ingestion solutions meet the needs of the business.

Troubleshoot and provide support for issues related to data quality and data ingestion solutions. This may involve debugging data pipeline processes, optimizing queries, or troubleshooting application performance issues.

Experience in working in Agile/Scrum methodologies, CI/CD tools and practices, coding standards, code reviews, source management (GITHUB), JIRA, JIRA Xray and Confluence.

Experience or exposure to design and development using Full Stack tools.

Strong analytical and problem-solving skills, excellent communication (written and oral), and interpersonal skills.

Bachelor's or master's degree in computer science or related field.