Oracle cloud engineer | Hong Kong, HK

Oracle cloud engineer

• Digital Transformation Project

• Young and international culture

• AI initiative Role Summary We are seeking an experienced Engineer to play a key role in the migration of our on-premise / legacy data warehouse to Oracle Cloud Infrastructure (OCI). This role will also contribute to AI-oriented initiatives, enabling advanced analytics, machine learning, and data-driven decision-making across the organization.

The ideal candidate combines strong data engineering fundamentals with cloud migration experience and a modern mindset toward AI, automation, and scalable analytics architectures.

Key Responsibilities

Data Warehouse Migration & Modernization

• Lead and execute the migration of enterprise data warehouse solutions to Oracle Cloud (OCI), ensuring data integrity, performance, and security

• Design and implement cloud-native data architectures using Oracle Autonomous Data Warehouse (ADW), Object Storage, and related OCI services

• Refactor and optimize existing ETL/ELT pipelines for cloud scalability and cost efficiency

• Migrate and modernize legacy SQL, PL/SQL, and BI workloads for cloud-based analytics

• Collaborate with architecture, infrastructure, and security teams to align with cloud standards and governance

Data Engineering & Platform Development

• Design and develop reliable, scalable data pipelines for structured and semi-structured data

• Implement data quality checks, lineage, and monitoring to ensure trusted datasets

• Support near real-time and batch processing use cases as required

• Optimize query performance, data models, and storage strategies in Oracle Cloud

• Develop reusable data frameworks and automation scripts for repeatable deployments

AI & Advanced Analytics Enablement

• Enable AI/ML use cases by preparing high-quality, analytics-ready datasets

• Partner with data scientists and analytics teams to operationalize machine learning models

• Support feature engineering, data versioning, and model training pipelines

• Integrate AI-driven insights into downstream analytics, dashboards, or applications

• Leverage Oracle Cloud AI/ML services or open-source ML frameworks where appropriate

Collaboration & Delivery

• Work closely with business stakeholders, BI teams, and data science teams to translate requirements into data solutions

• Contribute to agile delivery cycles, including sprint planning, estimations, and code reviews

• Document architecture, data flows, and operational procedures

• Mentor junior engineers and promote data engineering best practices Required Qualifications

• Bachelor's degree in Computer Science, Engineering, Data Science, or a related field

• 5+ years of experience in data engineering, data warehousing, or analytics engineering

• Hands-on experience with Oracle databases (e.g., Oracle DW, PL/SQL, SQL performance tuning)

• Proven experience migrating data platforms to Oracle Cloud Infrastructure (OCI) or similar cloud environments

• Strong expertise in ETL/ELT design, data modeling (dimensional, data vault, or similar), and SQL

• Experience building and maintaining production-grade data pipelines

• Solid understanding of cloud security, networking, and data governance concepts AI & Modern Analytics Experience (Required / Strongly Preferred)

• Experience supporting AI/ML or advanced analytics initiatives

• Familiarity with machine learning data preparation, feature engineering, and model deployment workflows

• Exposure to Python, Spark, or similar data processing frameworks

• Understanding of MLOps or data platforms that support AI lifecycle management Nice to Have

• Experience with Oracle Autonomous Database, OCI Data Integration, OCI Data Science, or OCI AI services

• Knowledge of open-source tools (e.g., Airflow, dbt, Spark)

• Experience with BI tools and analytics platforms (e.g., Oracle Analytics, Power BI, Tableau)

• Cloud certifications (Oracle Cloud, AWS, Azure, or GCP)

• Experience working in large-scale transformation or modernization programs What Success Looks Like

• Successful migration of legacy data warehouse workloads to OCI with minimal disruption

• Improved performance, scalability, and reliability of data platforms

• High-quality, AI-ready datasets enabling advanced analytics and machine learning use cases

• Strong collaboration across engineering, analytics, and business teams

• Adoption of modern data engineering and cloud best practices