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.