Senior Data Engineer

Cloudious•Charlotte, NC

About The Position

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 role focuses on building high-performance data pipelines, optimizing large-scale data processing workloads, and integrating enterprise data platforms with modern cloud analytics solutions. The engineer will oversee end-to-end implementation of data ingestion, transformation, quality, and distribution workflows, and lead development of ETL/ELT processes using enterprise ETL tools for high-volume, low-latency data needs. They will guide pipeline performance, scalability, and reliability improvements while ensuring observability, monitoring, and automation across data platforms. The position also involves partnering with Product Owners, Architects, Analysts, and cross-functional leaders to define requirements and translate business and data problems into scalable engineering designs. It includes hands-on technical leadership, mentoring engineers across multiple pods, leading code and solution reviews, and driving performance improvements and engineering excellence.

Requirements

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, complex SQL queries, and large-scale ETL/ELT processes.
Strong SQL and data modeling skills.
Experience with Oracle GoldenGate, CDC, or enterprise-scale data integration technologies.

Nice To Haves

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.

Responsibilities

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.
Oversee end-to-end implementation of data ingestion, transformation, quality, and distribution workflows.
Lead development of ETL/ELT processes using enterprise ETL tools for high-volume, low-latency data needs.
Guide teams in optimizing pipeline performance, scalability, and reliability, and ensure observability, monitoring, and automation across data platforms.
Partner with Product Owners, Architects, Analysts, and cross-functional leaders to define requirements and deliver scalable engineering designs.
Mentor and provide technical direction to engineers across multiple pods.
Lead code reviews, solution reviews, and performance improvement initiatives.