Data Engineer
Role Overview
Build, configure, and optimize data ingestion, transformation, and consumption workloads on a modern on-premises lakehouse platform.
The role calls for hands-on Spark development, enterprise data lake or lakehouse experience, and Informatica integration solutions.
The posting specifies full-time employment, KSA work location, and 5 to 10 years of experience.
Key Skills for This Role
Full Job Posting
Role overview
Build, configure, and optimize data ingestion, transformation, and consumption workloads on a modern on-premises lakehouse platform.
The role calls for hands-on Spark development, enterprise data lake or lakehouse experience, and Informatica integration solutions.
The posting specifies full-time employment, KSA work location, and 5 to 10 years of experience.
Responsibilities
- Design and develop scalable data ingestion and transformation pipelines; build, optimize, and maintain Spark-based ETL/ELT workloads.
- Develop data models and Apache Iceberg datasets; implement and support Informatica integration workflows.
- Enable analytics and data consumption with Denodo, Starburst, and/or Dremio.
- Build secure, governed, scalable pipelines and support batch and near-real-time processing.
- Implement data quality, metadata, and validation controls; collaborate with Platform Engineers on deployment and operationalization.
- Support migration, testing, and production rollout; troubleshoot and optimize pipeline performance, reliability, and scale.
Must-have skills
- Hands-on Spark development, Informatica integration, and enterprise data platform experience.
- Apache Spark (PySpark and/or Scala Spark), Apache Iceberg, Informatica Data Integration, MinIO, and Denodo.
- SQL, data modeling, data warehousing concepts, ETL/ELT development, and Git version control.
- The posting lists 5 to 10 years of experience.
Good-to-have skills
- Starburst and/or Dremio, data governance and metadata management, data quality frameworks, and CI/CD for data pipelines.
- Python, Kafka or streaming technologies, lakehouse architecture patterns, and enterprise data lake or lakehouse implementation experience.
- Performance tuning and optimization of large-scale data workloads.
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