Data Scientist

Saudi Aramco (ASC)

We are seeking a Data Engineering Specialist to join Downstream Global Optimizer (GO) team.

The Downstream Global Optimizer team drives commercial optimization across Saudi Aramco's integrated downstream value chain, including domestic and international refining assets. The GO team identifies and captures optimization opportunities to enhance system netback and deliver shareholder value. By building robust data infrastructure, automating pipelines, and enabling real-time analytics, the team supports agile decision-making in dynamic market conditions.

Your primary role is to design, develop, and maintain scalable data platforms that power the iGO (intelligent Global Optimizer) ecosystem. Your work will ensure seamless integration of structured/unstructured data, enable high-performance analytics, and provide reliable foundations for optimization models, machine learning pipelines, and enterprise reporting. You will focus on building and maintaining the data infrastructure that supports our data scientists. This role will involve designing and implementing scalable data pipelines, ensuring data quality, and managing data storage solutions. The specialist will work closely with data scientists and other teams to provide the necessary data infrastructure for advanced analytics and machine learning models.

Key Responsibilities

• Design and implement data architectures and efficient ETL (Extract, Transform, Load) processes to move and transform data from various sources into a format suitable for analysis.
• Ensure the accuracy, completeness, and consistency of data by implementing data validation and cleansing processes.
• Architect and manage data storage solutions, including data lakes and warehouses, to meet the needs of the organization.
• Work with data scientists and other stakeholders to understand data requirements and provide appropriate data solutions.
• Optimize data pipelines and architectures for scalability and performance, ensuring they can handle large volumes of data efficiently.
• Maintain thorough documentation of data systems, pipelines, and processes for clarity and continuity.
• Proficiency in languages such as Python, SQL, and Java or Scala.
• Experience with big data technologies like Apache Spark, Hadoop, Kafka, and Airflow.
• Knowledge of cloud services such as AWS, Azure, or GCP, including their data storage and processing services.
• Understanding of data warehousing concepts and experience with tools like Databricks, Microsoft data fabric and Cloudera.
• Familiarity with ETL tools and processes for data transformation and migration.
• Experience with CI/CD pipelines and version control systems like Git.