- 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