Data Engineer

Overview

We are seeking a skilled Data Engineer to design, build, and optimize data pipelines and architectures supporting analytics, AI, and enterprise applications. The ideal candidate has deep technical expertise in data modeling, ETL/ELT processes, and cloud data platforms, with a strong understanding of how to ensure data reliability, scalability, and security.

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Key Responsibilities

  •  Design and develop scalable data pipelines to collect, transform, and load structured and unstructured data from various sources.

  •  Architect and maintain modern data infrastructures (data lakes, data warehouses, streaming systems) using cloud platforms such as AWS, Azure, or Google Cloud.

  •  Collaborate with data scientists, analysts, cloud engineers and application teams to ensure data availability and integrity for analytical and operational use cases.

  •  Implement and optimize ETL/ELT workflows with tools such as Apache Airflow, Spark, Kafka, or dbt.

  •  Develop data models and schemas to support BI dashboards, AI/ML models, and business reporting.

  •  Ensure data governance, quality, lineage, and compliance with security and privacy regulations.

  •  Monitor and troubleshoot data systems performance, ensuring minimal downtime and high reliability.

  •  Automate data ingestion, transformation, and validation tasks to improve efficiency.

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Qualifications

  •  Bachelor’s or Master’s degree in Computer Science, Information Systems, or related field.

  •  3+ years of experience as a Data Engineer, ETL Developer, or in a related data engineering role.

  •  Proficiency in SQL, Python, and at least one cloud data platform (e.g., BigQuery, Snowflake, Redshift, Synapse).

  •  Experience with data orchestration tools (e.g., Airflow, Prefect, Dagster) and CI/CD pipelines.

  •  Strong understanding of data modeling (star/snowflake schemas) and database optimization techniques.

  •  Familiarity with data security, encryption, and data privacy standards (GDPR,PDPL, ISO 27001, NDMO in KSA).

  •  Knowledge of API integrations, streaming data (Kafka, Kinesis), and NoSQL systems is a plus.

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Preferred Skills

  •  Experience with containerization (Docker, Kubernetes).

  •  Familiarity with AI/ML data preparation workflows.

  •  Exposure to data cataloging and metadata management tools.

  •  Understanding of DevOps and CI/CD best practices for data pipelines.