Data Engineer

Designs, builds, and operates scalable, resilient data platforms that enable high-impact analytics, insights, and decision-making. Working with product, analytics, and engineering teams, the role rapidly prototypes, delivers, and runs reliable data pipelines and big data services in production

Key Responsibilities:

• Design and develop services for large-scale ingestion, storage, and management across RDBMS, NoSQL, logs, and event streams.
• Build and operate robust, scalable batch and streaming pipelines with Apache NiFi, Kafka, Spark, Flink, and Airflow.
• Develop highly concurrent, low-latency systems handling structured and unstructured data.
• Implement change data capture and data models (OLAP, dimensional, lakehouse) for analytics and AI.

Quality & Operations

• Ensure pipelines are deployable, observable, and operationally manageable, monitored against data SLAs.
• Implement data quality checks, lineage, documentation, and governance and access controls.
• Optimize the performance and cost of data storage and processing.
• Work with product teams and third-party providers on integrations, and with Data Scientists on datasets.
• Participate in sprint planning and delivery, and advise on emerging data technologies and best practices.
• Protect sensitive data (encryption, masking, least privilege) and comply with SAAL security and HSE policies.
• Data pipelines with Apache NiFi, Kafka, Spark, Flink, and Airflow (batch and real-time).
• Distributed data architectures, data modeling, OLAP design, and CDC (e.g., Debezium).
• Storage and messaging platforms (MinIO/S3, Kafka) and data formats (Avro, Parquet, ORC).
• Strong programming and scripting (Python, Scala, or Java) and advanced SQL.
• Agile delivery and Git version control. Excellent English; Arabic is an advantage.
• Enterprise analytics and reporting platforms; infrastructure and containerized deployments (Docker, Kubernetes).
• Lakehouse formats (Iceberg, Delta Lake), dbt, data quality tooling, and data preparation for GenAI (RAG).

Education:

• Bachelor's degree in Computer Science, Software Engineering, or a related field (preferred).

Certifications (preferred):

• Databricks, Confluent (Kafka), or cloud data engineering certifications are an advantage.

Experience:

• 3-5 years of professional experience as a Data Engineer or Big Data Engineer.