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 & PipelinesDesign 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 & OperationsEnsure 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.CollaborationWork 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 SkillsEnterprise 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.