Senior Specialist - Data Engineering

Role Overview

Senior Specialist - Data Engineering at Qiddiya Investment Company. This role transforms raw data into useful data systems through design, development, and maintenance of scalable infrastructure that supports analytics and decision-making across the organization.

Role Purpose

Transform raw data into useful data systems and align data infrastructure with business goals. Collaborate with cross-functional teams to ensure the availability, reliability, and accessibility of data for analytics and decision-making purposes while driving efficiency across the data platform.

Key Responsibilities

Data Pipeline & ETL Development

• Design, develop, and maintain scalable data pipelines and ETL processes using tools such as Apache Spark, Apache Kafka, and Apache Airflow to ingest, process, and transform large volumes of data from various sources.
• Deploy data pipelines on different data processing products: DataFlow (Apache Beam), DataProc (Hadoop/Spark), Data Fusion, and Cloud Composer (Airflow).
• Build distributed systems and data stores.
• Implement data quality checks, monitoring, and custom scripts to ensure the accuracy, completeness, and reliability of data.
• Propose and implement automation for repetitive tasks.

Data Storage & Architecture

• Implement and optimize data storage solutions, including data warehouses (e.g., Amazon Redshift, Google BigQuery), data lakes (e.g., AWS S3, Azure Data Lake Storage), and NoSQL databases (e.g., MongoDB, Cassandra).
• Work closely with data architects to design and implement efficient data models using dimensional modeling techniques (e.g., star schema, snowflake schema) that support business requirements and enable effective data analysis.
• Modernize data lakes and data warehouses.

Cloud Platform & Infrastructure

• Configure Google Cloud Platform services.
• Stay updated on emerging technologies and best practices in data engineering, including cloud-native solutions and serverless architectures.
• Contribute to the continuous improvement of data platforms and infrastructure.

Cross-Functional Collaboration

• Collaborate with and support data science, marketing, and customer success teams in data acquisition and tool integration.
• Collaborate with data scientists and analysts to understand data requirements and develop solutions to support advanced analytics and machine learning initiatives, including model training and deployment.
• Provide support for development teams in deployment-related topics.

Operations & Support

• Participate in troubleshooting and resolving data-related issues, ensuring timely resolution and minimal disruption to business operations.

Qualifications & Experience

• Bachelor's degree in Computer Science, IT, or similar field; Master's degree is a plus.
• Previous experience as a data engineer or in a similar role.
• Technical expertise with data models, data mining, and segmentation techniques.
• Strong understanding of data modeling, ETL processes, data warehousing concepts, and data integration techniques.
• Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform, and familiarity with related services (e.g., AWS Glue, Azure Data Factory, Google BigQuery).
• Google Cloud Platform experience is preferable.

Skills & Competencies

• Proficiency in programming languages such as Python, Java, or Scala.
• Experience with SQL and NoSQL databases.
• Solid knowledge of Google BigQuery for effective data processing.
• Excellent problem-solving skills and attention to detail.
• Ability to work effectively in a fast-paced environment and manage multiple priorities.
• Strong communication and interpersonal skills.
• Ability to collaborate effectively with cross-functional teams and stakeholders.
• Strong analytical skills and ability to combine data from different sources.
• Familiarity with machine learning methods.

Additional Information

• Data engineering cloud certification (e.g., Google Certified Data Engineer) is a plus.