AWS Data Analytics

Skills

Big Data Tools: Hadoop Cloudera (CDP) PySpark Kafka etc. Data Pipeline & Integration: Airflow Kafka Informatica Cloud (IICS) PowerCenter Data Governance: EDC AXON Stream-Processing Systems: Storm Spark-Streaming AWS Kinesis Databases: Relational Amazon DynamoDB Mongo DB Snowflake Postgres. AWS Cloud Services: EC2 S3 EMR RDS Lambda DMS Kinesis Glue Object-Oriented Languages: Python R

Description

1. 12 + Years’ experience in Enterprise Solutions and 5+ Years in Cloud preferably AWS.
2. Proficient understanding of distributed computing principles.
3. Strong knowledge and practical experience with AWS
4. Strong programming skills with experience in Webhook and API development using Node.js, Ruby, Python, Shell, and PowerShell
5. Familiarity with modern cloud application architecture
6. Exposure to cloud managed services and microservices like Function as a Service, Containers, and managed databases
7. Thorough understanding of ML, data analysis, data visualization, and event-driven architecture
8. Familiarity working with large systems
1. Experience with setting up load balancers, cloud networks, and virtual servers
10. Capable of working under tight deadline
11. Proficient in Solutioning for Real time Analytics

Responsibilities

• 1. Enhance our cloud capability by creating and implementing cloud application patterns
• 2. Develop and implement ways to move apps and workloads to the cloud
• 3. Work closely with business leads and product owners to understand solution requirements and identify architectural patterns
• 4. Write and develop cloud automation playbooks for managing and scaling containers, hosts, cloud services, and applications
• 5. Monitor compliance of cloud resources to see if they fit industry guardrails and best practices
• 6. Help other development and engineering teams resolve application to platform integration issues for Platform as a Service (PaaS) and Infrastructure as a Service (IaaS) services
• 7. Research and propose solutions for AWS data transformation, data connections, operational frameworks, and application integration
• 8. Work closely with lead architects and engineers to create and maintain architectural templates and build/operational documents
• 1. Work with DevOps and engineering teams to develop service catalogs
• 10. Based on customer technology landscape arrive at solutions which is more Agile, scalable, and Cost effective.
• 11. Provide Proactive proposals to customer on Cloud Strategy.
• 50. Understand Customer Problem Statements and leverage Ai/ML Capabilities for Solution.
• 1000. Build reusable artefacts and accelerators in Cloud
• 14. Collaborate with technical and business users to develop and maintain enterprise wide solutions and standards to provide data required for metrics and analysis