Data Engineering & Warehousing Engineer

Job Description: Data Engineering & Warehousing Engineer

Job Title: Data Engineering & Warehousing Engineer
Experience: 3–11 Years
Location: Riyadh - Onsite
Employment Type: Full-Time

Job Overview

We are seeking a highly skilled Data Engineering & Warehousing Engineer with 3–11 years of experience to design, develop, and maintain scalable data platforms and enterprise data warehouse solutions. The ideal candidate will have hands‑on expertise in building ETL/ELT pipelines, data integration, cloud‑based data platforms, and big data processing technologies. You will play a key role in enabling reliable, high-performance analytics and business intelligence solutions.

Key Responsibilities

• Design, develop, and optimize scalable ETL/ELT pipelines for structured and unstructured data.
• Build and maintain enterprise data warehouses, data lakes, and modern data platforms.
• Develop real‑time and batch data processing solutions.
• Integrate data from multiple internal and external sources while ensuring data quality and governance.
• Collaborate with Data Scientists, BI Developers, and business stakeholders to support analytical requirements.
• Optimize data storage, query performance, and pipeline reliability.
• Implement data security, monitoring, and governance best practices.
• Troubleshoot and resolve data pipeline and platform issues.
• Participate in architecture discussions and contribute to data platform modernization initiatives.

Required Technical Skills

Cloud Data Platforms

• Hands‑on experience with Google BigQuery and Dataflow and Dataproc and Pub/Sub .
• Experience with Azure Synapse and Azure Data Factory .
• Experience with Amazon Redshift and AWS Glue .

Data Processing & Streaming

• Strong experience with Apache Spark and Apache Kafka .
• Experience building batch and real‑time data processing pipelines.

Data Transformation

• Hands‑on experience with dbt or Oracle Data Integrator (ODI) for data transformation and orchestration.
• Experience implementing ETL/ELT best practices and reusable data models.

Databases & Data Warehousing

• Strong experience with Oracle or PostgreSQL .
• Expertise in SQL, relational database design, performance tuning, and query optimization.

Data Engineering

• Experience with data modeling, data governance, metadata management, and data quality frameworks.
• Knowledge of dimensional modeling and modern data warehouse architectures.

Qualifications

• Bachelor's degree in Computer Science, Information Technology, Data Engineering, Software Engineering, or a related field.
• 3–11 years of professional experience in Data Engineering, Data Warehousing, or Big Data technologies.
• Strong programming and scripting skills using SQL, Python, or similar languages.
• Excellent analytical and problem‑solving abilities.
• Experience working in Agile/Scrum development environments.

Preferred Skills

• Experience with cloud‑native data lake and lakehouse architectures.
• Knowledge of CI/CD pipelines and Infrastructure as Code (IaC).
• Familiarity with containerization technologies such as Docker and Kubernetes.
• Experience supporting machine learning and analytics workloads.
• Cloud certifications on AWS, Microsoft Azure, or Google Cloud Platform are a plus.

Key Technology Stack

• Google Cloud Data Services: BigQuery and Dataflow and Dataproc and Pub/Sub
• Azure Data Services: Azure Synapse and Azure Data Factory
• AWS Data Services: Amazon Redshift and AWS Glue
• Data Processing: Apache Spark and Apache Kafka
• Data Transformation: dbt or Oracle Data Integrator (ODI)
• Databases: Oracle or PostgreSQL
• Programming: SQL and Python
• Cloud Platforms: Google Cloud Platform or Microsoft Azure or Amazon Web Services (Preferred)