Senior Data Engineer

About the Role

We are collaborating with an expanding technology company to recruit a Senior Data Engineer based in Riyadh. This role involves a hands-on approach with comprehensive responsibility across the entire data platform lifecycle, including data ingestion, processing, storage, modelling, and governance. The successful candidate will create dependable data infrastructure leveraged by multiple technical and business teams and help define engineering best practices as the platform matures.

Responsibilities

• Design, develop, and maintain scalable batch and real-time streaming data pipelines.
• Architect data solutions encompassing ingestion, processing, storage, and transformation layers.
• Create reusable, reliable datasets and data models to support downstream teams and applications.
• Implement mechanisms for data validation, quality assurance, monitoring, lineage tracking, and governance.
• Ensure robustness through idempotent processing, deduplication, schema versioning, recovery strategies, and historical data accuracy.
• Establish access controls and safeguard sensitive information appropriately.
• Collaborate with engineering and infrastructure teams to manage production-grade data services.
• Optimize systems for performance, scalability, and cost-efficiency of infrastructure.
• Contribute actively to technical standards, write thorough documentation, and participate in code reviews.

Candidate Requirements

• Minimum eight years of professional experience in data engineering.
• Proficiency in Python and SQL, including experience with PySpark or similar distributed data processing frameworks.
• Hands-on production use of AWS data services such as S3, Glue, Glue Data Catalog, Athena, and Redshift.
• Demonstrated expertise designing and operating data lake or lakehouse architectures with layered transformations.
• Experience with ETL/ELT tools like Airbyte, Fivetran, or Meltano for data integration.
• Exposure to streaming or event-driven technologies such as Kinesis, MSK, SQS, SNS, DMS, or Debezium for change-data-capture.
• Familiarity with data modelling and transformation tools such as dbt or equivalents.
• Knowledge of columnar storage formats like Parquet and advanced table formats like Iceberg, Delta Lake, or Hudi.
• Experience using workflow orchestration platforms such as Managed Workflows for Apache Airflow (MWAA), Step Functions, Dagster, or Prefect.
• Comprehensive understanding of data quality management, security practices, governance protocols, and data lineage.

Additional Information

Experience in building robust data infrastructure supporting AI or other advanced data applications is considered valuable for this position.