AWS Data Engineer - Data Pipelines & Architecture

Skills
Years of Experience
Last Worked in Which Year
Rating 1-10
AWS
Glue
SNS/SQS
Python
Py-Spark
Data Lake
Cloud Watch
Cloud Trail
DB Design
SQL

Detail- JDs;

Lead the team technically to complete milestones on time.
Understand complete requirement, create Architecture and update all stakeholders.
Create POCs
Manage delivery/release to customer
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 Event Bridge, 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, Num Py, etc., to handle and process large datasets.
Work with various No

SQL 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