Cloud Engineer

As a Cloud Engineer at Noise, you will play a crucial role on the Analytics team, focusing on the optimization, maintenance, and execution of medium-complexity cloud-based data and analytics solutions. You will work within the Google Cloud Platform (GCP) infrastructure, contributing to the development and operational stability of systems, particularly those involving Machine Learning (ML) and Large Language Models (LLMs). You will execute defined tasks with a high degree of independence and support the wider team in maintaining engineering excellence.

Cloud Engineering and Execution

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• Solution Execution: Implement, optimize, and maintain robust cloud-based solutions for data analytics and insights based on established architectural patterns.
• ML/LLM Support: Execute defined tasks related to the deployment, maintenance, and scaling of software solutions utilizing Machine Learning (ML) models and Large Language Models (LLMs) within the GCP infrastructure.‍
• Infrastructure Maintenance: Develop and maintain essential cloud infrastructure components, ensuring operational efficiency, reliability, and adherence to security best
  
  practices.‍
• Automation and Scripting: Contribute to the development of automation scripts and use basic Infrastructure-as-Code (IaC) tools to streamline deployment and
  
  management processes.‍
• Troubleshooting and Optimization: Proactively monitor system performance, diagnose issues, and execute optimization strategies for existing cloud services and data pipelines.

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Technical Collaboration and Design Support

• Design Contribution: Work independently on assigned tasks and provide input to senior engineers on complex design considerations, seeking guidance where required to ensure system integrity.‍
• Documentation: Create and maintain clear technical and engineering documentation (including updates to UML diagrams) for system components, processes, and maintenance procedures.‍
• Cross-Functional Integration: Collaborate effectively with peer engineers and cross- functional teams to ensure the smooth integration and operation of cloud services and tools at the team/system level.

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Team and Education

• Process Adherence: Actively follow and contribute to the refinement of team processes, procedures, and best practices.‍
• Knowledge Sharing: Educate Strategy and Media teams on basic cloud solutions and implementation best practices relevant to your scope of work.‍
• Team Participation: Attend regular office meetings and developmental activities, including regular 1:1 meetings with your reporting manager.

Qualifications:

• Experience: 2-5 years of hands-on experience in software engineering, cloud engineering, or a related technical role.‍
• Cloud Platform Proficiency: Direct, practical experience with Google Cloud Platform (GCP) services.‍

Technical Proficiency:‍

• Strong proficiency in the primary programming language: Python.
• Practical experience with key GCP services: Cloud SQL, BigQuery, Dataflow, App Engine/Compute Engine, Pub/Sub.
• Foundational experience or strong desire to specialize in Machine Learning (ML) and Large Language Models (LLMs) within GCP.
• Strong command of SQL for data manipulation and analysis.
• Experience in pipeline-oriented ETL/ELT ingestion and build processes.
• Familiarity with UML for documenting system and process design.

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Methodology: Understanding of SDLC methodologies (Agile, SCRUM, etc.).

Industry Knowledge: Familiarity with Digital Analytics and Ad-Tech solutions, including

key systems/APIs (Google Campaign Manager, Google Ads, Facebook Marketing).

Certifications: Google Cloud Professional certification, particularly the Professional Machine

Learning Engineer (MLE), is a strong asset.

Soft Skills: Strong collaboration skills, an innate curiosity, and a commitment to

continuous learning in digital marketing, research, and analytics.

Working Conditions: This position operates under a hybrid work model. Employees are currently required to work from the office once per week to foster team collaboration and culture. While the role primarily allows for work-from-home arrangements, the required in-office frequency is subject to change based on evolving company and departmental needs.