About the job
Who We Are
At Foodics, we are revolutionizing the restaurant management space with our cutting-edge ecosystem and payment technology. Established in 2014, our headquarters are located in Riyadh, and we have expanded our presence across five countries, including the UAE, Egypt, Jordan, and Kuwait. Serving clients and partners in over 35 countries, we have processed an astounding 6 billion orders, solidifying our position as one of the fastest-growing SaaS companies in the MENA region. With three successful funding rounds, the latest being $170 million, we are enhancing our innovative capabilities to empower business owners.
The Role in Brief
As a Senior Data Engineer, you will architect and develop robust data pipelines, data contracts, and processing frameworks that drive analytics and machine learning features for Foodics. Collaboration with ML Engineers and platform teams will be crucial to ensure our data infrastructure is reliable, scalable, and compliant.
Your Responsibilities
- Design and implement scalable ETL/ELT pipelines utilizing cloud-native technologies.
- Establish and enforce data contracts in collaboration with domain squads and internal stakeholders.
- Work closely with ML Engineers on feature engineering and the creation of model-ready datasets.
- Incorporate monitoring, alerting, and observability into the data infrastructure.
- Maintain data security, lineage, and compliance with internal policies.
- Contribute to the development of onboarding toolkits and reusable data components.
What We Seek
- A minimum of 5 years of experience in data engineering, demonstrating expertise in scalable pipeline development.
- Proficiency in Python, SQL, and orchestration tools (such as Airflow, AWS Glue, and Step Functions).
- Experience with modern Lakehouse architecture and tools (e.g., S3, Redshift, Snowflake, dbt).
- Strong understanding of data modeling, lineage, observability, and governance frameworks, including dimensional modeling and schema evolution.
- Familiarity with ACID-compliant data formats like Apache Iceberg, Delta Lake, or Apache Hudi, and experience managing large-scale datasets.
- Proven ability to create fault-tolerant, testable, and maintainable pipelines within production environments.
- Excellent collaboration skills, working effectively with ML Engineers, Analysts, and Product Managers.
- Familiarity with CI/CD and infrastructure-as-code (Terraform/CDK preferred).
- Strong communication skills and a focus on documentation, standards, and continuous improvement.
Ideal Candidates Will Have
- Knowledge of MLOps integration and streaming technologies.
