Cloud Specialist (Data-to-AI Modernization)

PointStar; Kuala Lumpur; On-site; MYR 120,000 - 200,000; Full time

Job summary

PointStar is seeking a Cloud Specialist (Data-to-AI Modernization) who understands that AI begins with a modern data foundation. You will re-architect legacy environments into cloud-native ecosystems on Google Cloud, applying rigorous business logic to data handling and ensuring AI-ready pipelines.

From raw data migration to deployment of agentic and generative AI solutions, you will guide clients through the data-to-AI lifecycle, implementing Medallion architectures, Data Lakes, and scalable

Qualifications

• Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field.
• Advanced SQL and strong Python skills with ability to translate business logic into robust code.
• Experience with cloud data services (BigQuery, Dataflow) and modern ETL/ELT patterns.

Responsibilities

• Data Modernization & Migration: re-architect legacy SQL/NoSQL DBs and ETL to Google Cloud (BigQuery, Cloud SQL).
• Engineering Excellence & Business Logic: apply dimensional modeling and automate deployment workflows (DataOps).
• Bridging Data to AI: prepare data for Generative AI, vectorization, feature stores, and GenAI workflows.

Skills

SQL proficiency

Python

Strategic thinking

Communication

Mentorship

Vector embeddings

Education

Bachelors in Computer Science/Data Science/Engineering

Tools

BigQuery

Cloud Dataflow

Data Lakes

ETL/ELT tools

Job description

We are seeking a Cloud Specialist (Data-to-AI Modernization) who understands that the path to impactful Artificial Intelligence begins with a modern, high-quality data foundation.

This role is for those who believe "Data is the fuel for AI." You will be responsible for re-architecting legacy environments into cloud-native ecosystems, applying rigorous business logic to data handling, and ensuring that every pipeline you build is "AI-ready." You will guide clients from raw data migration to the deployment of agentic and generative AI solutions on Google Cloud.

Responsibilities

1.Data Modernization & Migration

• Legacy-to-Cloud Transition: Support the re-architecting and migration of legacy SQL/NoSQL databases and ETL processes to Google Cloud (BigQuery, Cloud SQL).
• Architecture Design: Implement scalable Medallion architectures (Bronze/Silver/Gold) and data lakehouses to ensure data is structured for both reporting and AI consumption.
• Pipeline Development: Design, build, and maintain scalable batch and streaming data pipelines (ETL/ELT), leveraging modern orchestration tools and integration patterns to ensure seamless data flow from diverse sources to AI-ready sinks.

2.Engineering Excellence & Business Logic

• Advanced Modeling: Apply dimensional modeling (Kimball/Inmon) to ensure data structures reflect actual business processes, making them intuitive for AI models and analysts alike.
• Automation & Lifecycle Management (DataOps): Implement automated deployment workflows and infrastructure management practices to ensure high reliability, environment consistency, and rapid iteration across the entire data-to-AI lifecycle.
• Optimization: Continuously monitor and tune query performance and storage costs to ensure a lean, efficient data environment.

3. Bridging Data to AI

• AI-Ready Data Preparation: Prepare data for Generative AI use cases, including vectorization, feature engineering, and the creation of Feature Stores for MLOps.
• GenAI Implementation: Assist in deploying AI solutions such as Gemini-powered agents, Vertex AI Search, and RAG (Retrieval-Augmented Generation) workflows.

4. Data Governance & Quality

• Reliability: Develop and implement robust data quality frameworks and validation protocols to monitor data health, ensuring that "trustworthy," high-fidelity data is available for downstream AI/ML consumption.
• Security & Compliance: Ensure all data solutions adhere to global compliance standards and internal security protocols, including encryption at rest/transit, granular IAM roles, and robust data masking techniques.
• Documentation: Maintain clear, professional documentation of data lineage, schemas, and system architecture to ensure transparency, auditability, and ease of maintenance.

Qualifications

Technical Requirements

• Education: Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field.
• Programming Mastery: Advanced-level SQL proficiency and strong Python skills, with the ability to translate business logic into robust code.
• Cloud Data Expertise: Proficiency knowledge of cloud data services, including BigQuery, Cloud Dataflow, and Data Lakes, along with proven experience in modern ETL/ELT tools and patterns.
• Professional Experience: Mid-Senior 3–5+ years of hands-on experience architecting and implementing complex data pipelines on GCP or equivalent cloud platforms.
• Strategic Thinking: Ability to translate abstract business requirements into technical specifications.
• Communication: Ability to explain complex technical concepts (like data lineage or vector embeddings) to non-technical stakeholders.
• Mentorship: Experience guiding junior engineers and conducting rigorous code reviews.

Preferred Qualifications:

• Google Cloud Certifications: Professional Data Engineer or Professional Machine Learning Engineer.
• AI Experience: Familiarity with LLM data preparation workflows and Vertex AI.