Senior Data Engineer - Oracle, Spark & Cloud Analytics

We are seeking an experienced Senior Data Engineer with deep expertise in Oracle-based analytical platforms and Apache Spark to support large-scale data modernization, migration, and analytics initiatives. The ideal candidate will have hands-on experience building high-performance data pipelines, optimizing large-scale data processing workloads, and integrating enterprise data platforms with modern cloud analytics solutions.

• 8+ years of experience in Data Engineering or related roles.
• Strong hands-on experience with Apache 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 with Google Cloud Platform (GCP) services such as BigQuery, 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.
• Ideal Candidate A hands-on data engineer with strong expertise in Oracle for analytical and data warehousing workloads, advanced Spark development and performance tuning skills, and experience building scalable cloud-based analytics solutions. Experience with Google Cloud and BigQuery is highly desirable.
• Bachelor's Degree in Computer Science, Information Technology, Data Engineering, Software Engineering, Computer Engineering, or a related technical field.
• Design, develop, and optimize large-scale data processing solutions using Apache 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.