AWS Data Engineer - Kinesis

Role: AWS Data Engineer - Kinesis (Band 3)

Design, develop, and maintain Real Time data streaming pipelines using AWS Kinesis for processing and analysing high-velocity data streams.

Collaborate with stakeholders to gather requirements and design efficient data streaming architectures for Real Time analytics, monitoring, and alerting.

Build and optimize Kinesis Data Streams, Kinesis Data Firehose, and Kinesis Data Analytics to process Real Time data from various sources.

Develop and implement ETL processes to transform, enrich, and aggregate streaming data for downstream consumption in data lakes or data warehouses such as AWS S3, Redshift, and DynamoDB.

Integrate Kinesis with other AWS services like Lambda, S3, and EMR for Real Time data ingestion, processing, and storage.

Optimize streaming applications for performance and scalability, ensuring efficient resource usage and minimal latency.

Develop and maintain robust error handling, data validation, and retry mechanisms within Kinesis pipelines to ensure data consistency and reliability.

Implement security best practices for data streaming, including encryption in transit and at rest, access control using IAM roles and policies, and data retention policies.

Monitor and troubleshoot AWS Kinesis streams using CloudWatch metrics, alarms, and logs, ensuring high availability and minimal downtime.

Collaborate with DevOps teams to automate the deployment and scaling of streaming pipelines using AWS CloudFormation, Lambda, and CI/CD tools.

Work closely with data analysts and data scientists to ensure Real Time data is processed and delivered for immediate insights and reporting.

Implement data partitioning, sharding, and key management strategies to handle high-throughput data streams and ensure scalability.

Optimize the cost of Real Time data processing by tuning the configurations of Kinesis and associated AWS services like Lambda and Firehose.

Stay current with AWS updates and best practices for streaming data, recommending improvements to the architecture as new features and services become available.

Provide guidance and mentorship to junior engineers in Real Time data processing, ensuring best practices in streaming technologies and AWS cloud services.

Create and maintain detailed technical documentation for data pipelines, including data flow diagrams, stream processing logic, and operational guidelines.