Cloud Engineer - AWS

Responsibilities:

• Collaborate with Engineering Teams: Work closely with our engineering teams to identify, design, and implement the most efficient and cost-effective cloud-based solutions that align with the company's objectives and technical requirements.
• Establish Best Practices: Define, document, and promote best practices and strategies for application deployment, infrastructure management, and continuous improvement of our cloud environment.
• Mentorship and Guidance: Provide strategic guidance, thought leadership, and hands-on mentorship to developer teams, fostering a culture of continuous learning and enhancing the organization's overall cloud proficiency.
• Ensure Performance and Reliability: Maintain high standards of application performance, scalability, and availability while upholding best practices for code quality and efficient system design.
• Cloud Security Management: Oversee the configuration and management of cloud environments, ensuring compliance with company security policies and industry best practices.
• Collaborate with Lead Solution Architects: Develop and implement AWS-based technical solutions for large-scale data processing, intensive statistical modelling, and advanced analytics. Design a scalable, secure, and highly available architecture tailored to project needs.
• Full Software Development Lifecycle: Participate in planning, requirements gathering, design, development, testing, and quality assurance of AWS solutions. Apply agile methodologies to deliver reliable cloud-native applications efficiently.
• Serverless Application Design: Architect and deploy serverless solutions using AWS services like AWS Lambda, API Gateway, DynamoDB, Step Functions, EventBridge, SQS, and SNS. Ensure applications are optimized for performance and cost-efficiency.
• Infrastructure as Code (IaC): Use tools like AWS CloudFormation and Terraform to define, deploy, and manage cloud infrastructure. Implement best practices for version control, reusability, and infrastructure scaling.
• Data Processing and ETL: Use AWS Glue for data transformations, schema discovery, and ETL operations. Manage real-time data streams with Amazon Kinesis or Amazon Managed Streaming for Apache Kafka (MSK), and deploy EKS for container orchestration.
• Database Migration and Management: Utilize AWS Database Migration Service (DMS) to transfer data from on-premises databases to AWS or migrate data between AWS databases. Ensure optimal performance and data integrity.
• CI/CD and Automation: Automate deployment pipelines using AWS CodePipeline, CodeBuild, and CodeDeploy. Implement continuous integration and continuous deployment (CI/CD) practices to accelerate software delivery.
• Security and Compliance: Implement security best practices with AWS Identity and Access Management (IAM), virtual private cloud (VPC) design, encryption, and compliance standards. Conduct regular security assessments and audits.
• Monitoring and Troubleshooting: Set up monitoring, logging, and alerting using AWS CloudWatch, AWS X-Ray, and other observability tools. Quickly diagnose and resolve issues, perform root cause analysis, and document solutions.
• Incident Management and Prevention: Proactively troubleshoot and resolve infrastructure incidents, implementing preventive measures to improve system reliability and availability.
• Training and Enablement: Educate and mentor teams on cloud adoption, AWS services, and best practices. Provide training sessions to ensure knowledge transfer and promote a cloud-first culture.
• AWS Account Management: Ensure AWS resources adhere to governance policies. Optimize costs and enforce security and operational standards across accounts.
• Architect for Scalability and High Availability: Build scalable and highly available solutions using AWS services such as EC2 RDS, and S3 For seamless scaling, consider a microservices architecture using ECS, AWS Fargate, and AWS Lambda.
• Disaster Recovery Planning: Develop robust disaster recovery strategies using AWS Backup, cross-region replication, and snapshot management. Test and validate recovery procedures regularly.
• DevOps and Continuous Improvement: Integrate DevOps practices by leveraging AWS services. Continuously improve deployment speed, application performance, and resource efficiency.

Requirements:

• Experience: 8-12 years of hands-on experience in architecting, designing, developing, and implementing cloud solutions on AWS platforms.
• Education: Bachelor's degree (or equivalent) in computer science, information technology, mathematics, or a related field.
• Certifications: AWS Certification(s), such as AWS Certified Solutions Architect, AWS Certified DevOps Engineer, or relevant specialty certifications.
• AWS Well-Architected Framework: Strong understanding of and experience applying the five pillars of the AWS Well-Architected Framework (operational excellence, security, reliability, performance efficiency, and cost optimization).
• API and Web Services: In-depth knowledge and proven experience with web services, APIs, RESTful architectures, and application integration.
• Architecture and Design: Familiarity with Service-Oriented Architecture (SOA), microservices, and modern database architectures is a vital asset. Knowledge of machine learning and advanced analytics is also a vital asset.
• Serverless Expertise: Demonstrated experience with AWS serverless services, such as API Gateway, EventBridge, Step Functions, SQS, SNS, AWS Lambda, and serverless data storage options like DynamoDB.
• Collaboration: Proven ability to work collaboratively with cross-functional teams, including business analysts, software developers, product managers, data scientists, and subject-matter experts.
• Resource Optimization: Proven track record of optimizing AWS resources for cost-efficiency and performance, including appropriate instance selection, auto-scaling, and efficient usage of storage and compute services.
• Problem-solving: Exceptional problem-solving skills, with the ability to identify and resolve issues proactively to maintain business productivity.
• Data Management: Candidates should have direct experience with data modelling and be familiar with building or managing data lakes and data warehouses. Experience with services like AWS Lake Formation, Redshift, or Athena is an added advantage.