Job Description

Job Summary

Job Description:

• Develop Services to enable data ingestion from and synchronization with system which exposes required data access mechanisms ensuring near-real-time updates
• Ingest data from multiple sources using the python and any other ETL tools
• Design and implement an event-driven architecture using AWS EventBridge, Kafka, or SNS/SQS for real-time data streaming
• Design, implement, and maintain scalable data pipelines that integrate both on-prem and AWS cloud environments.
• Develop efficient Python scripts and applications using libraries like pandas, NumPy, etc., to handle and process large datasets.
• Work with various NoSQL databases (e.g., MongoDB, Cassandra, DynamoDB) to support high-performance data storage and retrieval.
• Develop and deploy applications in a cloud-native architecture, leveraging modern cloud technologies for scalability and resilience.
• Continuously monitor data workflows and systems, troubleshoot issues, and optimize performance for reliability and scalability

Transition existing pipeline to MSSQL server

• Collaborate with the business application owner on the existing data architecture, including data ingestion, data pipelines, business logic, data consumption patterns, and analytics requirements
• Design and document the target data architecture, pipelines, processing and analytics architecture
• Identify opportunities for optimization and consolidation
• Collaboration with data team on decomposition of business logic and data transformation patterns

Key Responsibilities

Job Description:

• Develop Services to enable data ingestion from and synchronization with system which exposes required data access mechanisms ensuring near-real-time updates
• Ingest data from multiple sources using the python and any other ETL tools
• Design and implement an event-driven architecture using AWS EventBridge, Kafka, or SNS/SQS for real-time data streaming
• Design, implement, and maintain scalable data pipelines that integrate both on-prem and AWS cloud environments.
• Develop efficient Python scripts and applications using libraries like pandas, NumPy, etc., to handle and process large datasets.
• Work with various NoSQL databases (e.g., MongoDB, Cassandra, DynamoDB) to support high-performance data storage and retrieval.
• Develop and deploy applications in a cloud-native architecture, leveraging modern cloud technologies for scalability and resilience.
• Continuously monitor data workflows and systems, troubleshoot issues, and optimize performance for reliability and scalability

Transition existing pipeline to MSSQL server

• Collaborate with the business application owner on the existing data architecture, including data ingestion, data pipelines, business logic, data consumption patterns, and analytics requirements
• Design and document the target data architecture, pipelines, processing and analytics architecture
• Identify opportunities for optimization and consolidation
• Collaboration with data team on decomposition of business logic and data transformation patterns

Skill Requirements

Role: Data Engineering AWS

Primary Skills: AWS Glue, Kafka, or SNS/SQS, Python / PySpark, Data lake, Cloudwatch, Cloudtrail, SNS/SQS, DB design and SQL

Secondary Skills: AWS IAM, EKS

Other Requirements