Data Engineer

Job Summary

Senior And Lead Data Engineer

Data Platform & Pipeline Engineering

• Design, develop, and maintain
• scalable ETL/ELT pipelines
• using Python, SQL, dbt, and modern orchestration tools.
• Build and optimize
• cloud-based data warehouses
• (BigQuery or equivalent) supporting high-volume analytical workloads.
• Implement
• robust data modeling
• (dimensional and analytical models) to enable self-service analytics and AI use cases.
• Ensure
• data quality, integrity, and freshness
• through automated validation frameworks and monitoring.

Ai-Driven Automation & Innovation

• Design and deploy
• AI-powered internal tools and agents
• to automate data engineering workflows, incident diagnosis, and root cause analysis.
• Integrate AI agents with collaboration tools (e.g., Slack) to improve operational efficiency and reduce incident resolution time.
• Apply AI-assisted development techniques to accelerate delivery, improve code quality, and reduce manual effort across teams.

Performance & Cost Optimization

• Lead
• cloud cost optimization initiatives
• , including query optimization, architectural redesign, and usage monitoring.
• Drive measurable reductions in data platform operational costs while maintaining performance and reliability.

Cloud & Devops

• Build and deploy data services using
• containerized and CI/CD-driven workflows
• .
• Collaborate with DevOps and platform teams to ensure scalable, secure, and reliable cloud infrastructure.
• Support multi-cloud or hybrid environments as required (GCP, Azure).

Leadership & Collaboration

• Provide
• technical leadership and mentorship
• to data engineers.
• Establish
• SOPs, best practices, and engineering standards
• across the data function.
• Partner closely with product, analytics, and business stakeholders to translate requirements into scalable data solutions.

Technical Skills

• **Programming:**
• Python (expert), SQL (expert), Shell scripting (advanced).
• **Data Engineering:**
• dbt, ETL/ELT pipelines, data warehousing, data modeling, data quality frameworks.
• **Cloud Platforms:**
• Google Cloud Platform (BigQuery, Cloud Run) – strong experience; Azure familiarity is a plus.
• **Orchestration & Automation:**
• Airflow or equivalent.
• **DevOps & Tooling:**
• Git, Docker, CI/CD pipelines.
• **AI & ML Exposure:**
• AI agent frameworks, ML pipelines, model lifecycle management, observability tools.

Data Management

• Cost optimization and performance tuning.
• Data governance, cataloging, and best-practice documentation.

Education

• Bachelor’s degree in Computer Science or related field.
• Master’s or Postgraduate qualification in Data Science is preferred.
• Nice to Have
• Experience delivering
• AI/ML pipelines
• in production environments.
• Exposure to
• government, public sector, or regulated enterprise data platforms
• .
• Experience building
• observability and monitoring tools
• for data platforms.
• Prior work on
• Arabic-language or regional datasets
• .