Information Technology_USA - USA_Developer
Real Soft•Jacksonville, FL
•Onsite
About The Position
We are looking for a highly skilled Senior Data Engineer with deep expertise in Apache Spark, Scala, and PySpark to build and operate large scale batch and streaming data processing systems. The role has a strong emphasis on real time streaming architectures using Kafka and Spark Structured Streaming, alongside ingestion and orchestration with Apache NiFi and scalable storage using Apache Ozone and Ceph. This position is ideal for engineers who enjoy solving complex performance, scalability, latency, and reliability challenges in production data platforms.
Requirements
- Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience
- Strong hands on experience with Apache Spark in production environments
- Advanced proficiency in Scala and PySpark
- Solid understanding of distributed systems and data processing at scale
- Strong experience with Kafka based streaming architectures
- Hands on experience with Spark Structured Streaming
- Experience building batch and real time pipelines
- Hands on experience with Apache NiFi for data ingestion and flow management
- Strong SQL skills and experience working with structured and semi structured data
- Experience working with object storage or distributed storage platforms
- Proficiency with Linux, shell scripting, and Git based version control
Nice To Haves
- Experience with Apache Ozone and/or Ceph as storage backends for analytics workloads
- Experience implementing exactly once / at least once streaming semantics
- Strong background in Spark performance tuning (CPU, memory, I/O, shuffle)
- Experience supporting mission critical production systems with strict SLAs
- Familiarity with CI/CD pipelines and automated testing for data applications
- Experience designing observability for streaming systems (lag, throughput, backpressure)
Responsibilities
- Design, develop, and maintain large scale Spark applications using Scala and PySpark
- Build and operate streaming heavy data pipelines using Kafka and Spark Structured Streaming
- Implement stateful streaming patterns including windowing, watermarking, late data handling, and checkpointing
- Develop robust event replay and reprocessing workflows using Kafka offsets and partitions
- Build ingestion and routing flows using Apache NiFi, including Kafka based ingestion patterns
- Implement end to end ETL/ELT pipelines with strong emphasis on low latency, fault tolerance, and scalability
- Optimize Spark jobs through partitioning strategies, memory tuning, shuffle optimization, and efficient data formats
- Integrate Spark workloads with distributed object storage systems such as Apache Ozone and Ceph
- Ensure data quality, consistency, and auditability through validation, reconciliation, and metadata capture
- Collaborate with platform, infrastructure, and operations teams on production readiness and capacity planning
- Support production systems, including monitoring, incident analysis, and root cause resolution
- Contribute to reusable frameworks, coding standards, and engineering best practices
- Participate in architecture reviews, code reviews, and technical documentation
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
Upload and Match Resume
What This Job Offers
Job Type
Full-time
Career Level
Senior
