About Position:

We are seeking a highly skilled Spark Data Engineer with robust hands-on experience in building scalable data processing solutions using Apache Spark. The ideal candidate should have experience in Java/Scala development, data engineering best practices, and CI/CD implementation for data platforms.

Role: Data Engineer
Location: All Persistent Locations
Experience: 7 to 13 Years
Job Type: Full-Time Employment

What You'll Do:

Design, develop, and optimize large-scale data processing pipelines using Apache Spark.
Build and maintain batch and real-time data ingestion and transformation workflows.
Develop scalable data solutions using Java and/or Scala.
Analyze and improve Spark job performance, resource utilization, and execution efficiency.
Collaborate with data architects, business analysts, and cross-functional teams to understand requirements and deliver data solutions.
Implement coding standards, unit testing, and best practices for data engineering projects.
Build and maintain CI/CD pipelines for automated deployment and testing of data applications.




Troubleshoot production issues and provide timely resolution.
Participate in code reviews and ensure high-quality, maintainable code.
Support cloud-based and distributed data processing environments.

Expertise You'll Bring:

Apache Spark (Core, Spark SQL, Data Frames, Performance Tuning)
Java (Intermediate to Advanced)
Scala (Basic to Intermediate)
CI/CD tools such as Jenkins, GitHub Actions, GitLab CI, Azure DevOps, etc.
Git version control
Experience with Hadoop ecosystem (HDFS, Hive, YARN).
Experience working on data migration or modernization projects.
Knowledge of cloud platforms such as AWS, Azure, or GCP.
Understanding of data warehousing and ETL/ELT concepts.
Strong problem-solving and analytical skills.
Look for candidates who can demonstrate:
Hands-on Spark development experience (not just listed as a skill).
Performance tuning and optimization of Spark jobs.
Real project experience using

📌 Spark Data Engineer (Pune)
🏢 Persistent Systems
📍 Pune