AWS SME Engineer
IT Engineer V AWS SME Engineer::::::::::::: Hybrid Reston VA
The job description and details are below. This req will be distributed after the call (and fully approved). Please come prepared to ask any questions you might have about this position. Please feel free to forward this invite on to anyone else on your team.
JOB DESCRIPTION
Data Engineering and Advance Analytics Enablement team is looking for an AWS SME Engineer that will be playing a key role in helping on enabling and expanding Analytics platforms for FannieMae research community and bring in key transformational changes to how we deploy Analytics Tools using AWS cloud native architecture. This role will support a full range of systems engineering activities including technical requirements analysis engineering review activities and special projects that will require individual ownership of Python/R/SAS associated COTS deployments in AWS.
Duties & Responsibilities
- Participate and drive scoping and technical requirements gathering sessions for developing and deploying Python R or SAS
- Product Development including designing products developing product roadmaps translating design requirements prototyping etc.
- CI/CD development and management of analytics containers and infrastructure provisioning automation and operational excellence in the cloud
- Optimize Analytics containers (Python/R) to maximize velocity efficiency and agility on cloud native services like ECS EKS or AWS BATCH
- Build test and deploy new Infrastructure as Code leveraging all things AWS: Terraform Gitlab/Jenkins CloudFormation Templates Code Pipeline ECS/EKS (Docker) Lambda (R/Python) CloudWatch Route53 S3 and more
- Enhance monitor performance of systems in AWS including overall system health reliability performance and cost
- Operational Excellence including improving and overseeing operations
- Develop & maintain technical documentation including system diagrams operational procedures & user guides
- Leverage serverless computing autoscaling and rightsizing the infrastructure
- Cost Optimization for Storage EC2 instances and other resources
Required qualifications:
- 7 years of production experience with setting up CI/CD pipelines in systems like Jenkins AWS Code Pipeline
- 5 years of production experience with core AWS services with experience automating AWS infrastructure
- Experience with AWS cloud migrations
- Strong knowledge of core AWS services such as S3 EC2 CloudFormation RDS Lambda Step Function CloudWatch EventBridge scheduler
- Experience in Software Development preferably in Python/Java
- Solid understanding of AWS networking (VPC NACLs SGs routing etc.)
- Fluency in AWS Cli usage and AWS SDKs for Python/Java
- Python/Java knowledge sufficient to code for AWS SDKs
- HandsOn Shell scripting to create and manage automation scripts
Tools:
- Python
- AWS Tech Stack
- DevOps
Other Competencies:
- Knowledgeable in the AWS Well Architected Framework
- Strong analytical organizational problemsolving and timemanagement skills
- Strong communication (verbal and written) and listening skills
- Strong teamwork planning and coordination skills
- Selfmotivation adaptability and the ability to meet aggressive deadlines
- Ability to independently research and resolve technical problems in a complex IT environment with local and remote groups
- Relationship Management including managing and engaging stakeholders customers and vendors building relationship networks contracting etc.
- Experience gathering accurate information to explain concepts and answer critical questions
- Influencing including negotiating persuading others facilitating meetings and resolving conflict
Fieldglass Job Title: | IT Engineer V |
Managers Job Title: | AWS SME Engineer |
Quantity of positions: | 1 | New Role or Backfill: Comments for Budget Approvers/details. | Backfill |
Open to subcontractors Y/N | Y | Education Requirements: | See JD |
Certification Requirements: | See JD | Max Bill Rate to FNMA: | Will be stated on call |
Remote or Hybrid If Hybrid site location and cadence for in office | Hybrid | Assignment Duration (Up to 24 months) | 12 Months |
Potential for extension Y/N | N | Conversion potential Y/N | N |