Description

Key Responsibilities: 

• Design, develop, and optimize large-scale data processing solutions usingApache Spark (PySpark/Scala)and Python.
• Build and maintain scalable ETL/ELT pipelines for analytical and reporting workloads.
• Optimize query performance, Spark jobs, and end-to-end data processing pipelines for high-volume batch and near real-time workloads.
• Design and implement data integration, replication, and ingestion solutions using technologies such as Oracle GoldenGate, Kafka, and CDC frameworks.
• Collaborate with application, data, and analytics teams to develop scalable data models and processing frameworks.
• Drive performance tuning initiatives across Oracle, Spark, and distributed data processing environments.

Required Qualifications

• 8+ years of experience in Data Engineering or related roles.
• Strong hands-on experience withApache Spark (PySpark and/or Scala)and Python.
• Deep understanding of Oracle databases supporting OLAP, data warehousing, and analytical workloads.
• Proven experience tuning and optimizing:
  • Spark applications and execution plans
  • Complex SQL queries
  • Large-scale ETL/ELT processes
  • High-volume batch processing workloads
• Strong SQL and data modeling skills.
• Experience with Oracle GoldenGate, CDC, or enterprise-scale data integration technologies.

Preferred Qualifications: 

• Experience with Oracle Exadata and large-scale Oracle analytics platforms.
• Experience withGoogle Cloud Platform (GCP)services such asBigQuery, Dataproc, Cloud Storage, Pub/Sub, and Dataflow.
• Experience integrating Oracle with cloud data platforms, data lakes, Kafka, Hadoop, or BigQuery.
• Experience with Azure, OCI, or multi-cloud data architectures.
• Experience supporting large-scale data migration and modernization programs

Key Skills

Education

Bachelor's degree