Data Engineer

Job Purpose: 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: Data Platforms & Pipelines • 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. Collaboration • 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. Essential Skills • 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. Desirable Skills • 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.