About the job
About Us
At Foodics, we are revolutionizing the restaurant management ecosystem as a premier provider of payment technology. Established in 2014, with our headquarters in Riyadh and offices spanning across five countries, including the UAE, Egypt, Jordan, and Kuwait, we proudly serve clients and partners in over 35 nations worldwide. Our cutting-edge products have facilitated the processing of over 6 billion orders, solidifying Foodics as one of the fastest-growing SaaS companies in the MENA region. Recently, we completed our third funding round, securing $170 million in the largest SaaS funding raise in MENA, further enhancing our innovation capabilities to support business owners effectively.
Your Role
As a Senior Data Engineer, you will take charge of designing and constructing robust data pipelines, enforcing data contracts, and creating processing frameworks that drive analytics and machine learning features across Foodics. You will collaborate closely with ML Engineers and platform teams to guarantee the reliability, scalability, and governance of our data infrastructure.
Your Responsibilities
- Architect and implement scalable ETL/ELT pipelines utilizing cloud-native tools.
- Establish and uphold data contracts with domain teams and internal stakeholders.
- Partner with ML Engineers on feature engineering and preparing datasets for models.
- Develop monitoring, alerting, and observability features within the data infrastructure.
- Ensure data security, lineage, and compliance with internal standards.
- Contribute to the creation of onboarding toolkits and reusable data components.
Your Profile
- Minimum of 5 years of experience in data engineering, demonstrating proficiency in developing scalable pipelines.
- Expertise in Python, SQL, and orchestration tools (e.g., Airflow, AWS Glue, Step Functions).
- Familiarity with modern Lakehouse architectures and tools (e.g., S3, Redshift, Snowflake, dbt).
- In-depth understanding of data modeling, lineage, observability, and governance frameworks (e.g., dimensional modeling, schema evolution, ML feature stores).
- Proficiency with ACID-compliant data formats such as Apache Iceberg, Delta Lake, or Apache Hudi and experience in managing large datasets with time travel, schema evolution, and transactional guarantees.
- Experience in developing fault-tolerant, testable, and maintainable data pipelines in production settings.
- Ability to work effectively in cross-functional teams alongside ML Engineers, Analysts, and Product Managers.
- Familiarity with CI/CD practices and infrastructure-as-code (preferably Terraform/CDK).
- Excellent communication skills with a focus on documentation, standards, and continuous improvement.
Ideal Candidates Will Have
- Knowledge of MLOps integration and streaming technologies.
