Job Summary

We are seeking an Analytics Engineer to design and build Python-based solutions on Google Cloud. In this role, you will develop scalable cloud data services, create and optimize data models, and implement automation workflows. You will leverage prompt engineering and hosted LLM services (e.g., Gemini) to build templates to extract information and AI-powered automation. You will also design APIs and data pipelines to support analytics and AI initiatives, collaborating closely with business and product teams to translate requirements into reliable, practical solutions.

Key Responsibilities

• \tDevelop high-quality Python code with strong emphasis on testing, performance, and maintainability.

• \tBuild and operate cloud-based data services using GCP tools such as BigQuery and DataProc.

• \tDesign and optimize database models using SQL.

• \tCreate and maintain prompt templates for analytics and automation, applying prompt engineering to extract actionable insights from raw text and email content.

• \tDevelop data pipelines and APIs to enable analytics and product development.

• \tCollaborate with cross-functional teams to convert business needs into technical solutions.

• \tSolve complex problems using large and diverse datasets.

Education

Bachelor's degree in computer science, Engineering, or a related field required.

Experience

• \tOpen to both fresh graduates and experienced professionals. Candidates with hands-on experience in analytics or software development - particularly in Python and cloud-based solutions, will be preferred. Fresh graduates should be able to demonstrate a relevant portfolio of projects showcasing their skills.

• \tProven expertise in designing, building, and maintaining robust data-driven applications and data products.

• \tStrong commitment to code quality, testing practices, and production-ready deployments.

• \tHands-on experience with prompt engineering and integrating hosted LLM services (e.g., Gemini APIs) to design end-to-end automation workflows.

• \tPractical experience with CI/CD pipelines for code productization, including automated testing, packaging, and deployment.