Job Summary
We are seeking a Senior / Lead Data Engineer to design, build, and scale AI-powered, cloud-native data platforms that support enterprise analytics, machine learning, and business-critical decision-making.
The ideal candidate will bring deep expertise in data engineering, cloud data warehouses, cost optimization, and AI-assisted development, with a proven track record of delivering high-impact, revenue-generating data platforms and intelligent automation solutions at scale.
Key Responsibilities
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.
Required Skills & Qualifications
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.
