Objective

No matter how cool your Big Data solution is - w/o the solid ground underneath, it will be no more resilient than Three Little Pigs houses. In Neurons Lab, developing such a solid foundation is the central task of Cloud Infrastructure Engineers (IE). All of them have 4+ years of experience and are well-prepared to help you build a secure, reliable, sustainable Cloud Landing Zone, establish Development Operations to support its further growth, and fully uncover the potential of DE-established DataOps.

About project

The customer is developing a comprehensive, first-in-class Geospatial Data Warehouse SaaS. Geospatial data is traditionally hard to work with and requires years of experience with highly-specialized tools. These tools are highly fragmented - each solves only a little piece of the puzzle, uses different (often incompatible) formats for data, and exposes it through interfaces not supported by standard analytical tools. The customer aims to unite this fractured world and give its Customers a powerful, convenient instrument to collect, process, store, analyze, and visualize their geospatial data. All to help them uncover the extra value hidden in their data without the undifferentiated heavy lifting of running their own infrastructure or studying rocket science.

Challenges & Responsibilities

Architecting, building, and maintaining cost-efficient, scalable cloud environments for customers.
Understanding the customer's business objectives and creating cloud-based solutions to facilitate those objectives.
Conducts Well-Architected Reviews and audits customer AWS Accounts
Participate in the SOW creation process along with the Sales team, Delivery Manager, and engineers
Conducts customer-facing architecture assessment meetings to gather business and technical requirements, aligns those cloud best practices, architect a solution and, write Epics and User Stories in collaboration with the Team
Participates in internal and external projects Stand-Ups as a Product Owner from the project's inception to completion
Support quality development practices and pursue new and better ways to build and deploy software and ML/AI models

Skills

Foundational / must-have skills:

[ ] DataOps: AWS Lambda
[ ] DevOps: Python | AWS CDK | AWS Control Tower, Organizations, CloudTrail, Config; Amazon CloudWatch, SNS | AWS Transit Gateway, PrivateLink; Amazon VPC | Git | AWS IAM, KMS, Secrets Manager | Amazon S3, CloudFront, Route 53 | AWS CodeBuild, CodeArtifact, CodeDeploy, CodePipeline

Advanced / nice-to-have skills:

[ ] DataOps: AWS Lambda for Python | Amazon EventBridge | AWS Step Functions; Apache Airflow, Astronomer
[ ] DevOps: AWS CDK for Python | AWS Marketplace, Application Cost Profiler | AWS Cost Explorer, Budgets, Cost & Usage Reports, Cost Anomaly Detection, Savings Plans & Reserved Instances | AWS Backup, License Manager | Amazon API Gateway; REST APIs | AWS X-Ray, Lambda Powertools; Amazon OpenSearch Service; Sentry, Datadog | AWS IAM Identity Center (aka SSO), IAM Access Analyzer, Security Hub, Resilience Hub; Amazon Cognito, Cognito External Identity Providers, GuardDuty, Detective | GitLab, NPM, Webpack, Babel, JavaScript CI / CD, TypeScript CI / CD, ReactJS CI / CD, NextJS CI / CD, Figma CI / CD & Figma Tokens; AWS Amplify | AWS Web Application Firewall (WAF), Global Accelerator; Amazon S3 Website Hosting, Shield
[ ] Collaborative, consultative, and committed to continuous improvement and learning in a team-based environment
[ ] Excellent interpersonal communication skills to explain complex technical topics in an easily comprehensible manner
[ ] Customer-focused, analytical, detail-oriented, and a positive “can-do” attitude

Knowledge

One or more AWS certifications, preferably. AWS Solutions Architect Professional AWS Data and Analytics - Specialty or AWS Certified Machine Learning - Specialty

Experience

4+ years of commercial, hands-on experience in many domains (Data Analytics, Engineering, DevOps, Machine Learning)

Terms & conditions

Allocation: 0.5 FTE (part-time)

Time zone: no matter

Candidate's Location: remote

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