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Spark-Java, DBX

Spark-Java, DBX

Infosys
Early Applicant
  • Posted 10 hours ago
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Job Description

Spark-Java, Databricks

Key Responsibilities:

  • Design, develop, and maintain Spark-based data processing jobs using Java aligned to business and technical requirements.
  • Build and enhance ETL workflows, ensuring accuracy, completeness, and consistency of processed datasets.
  • Implement integrations and workflows on DBX, supporting scalable execution and operational reliability.
  • Optimize Spark jobs for performance (partitioning, caching, shuffle tuning) and cost efficiency.
  • Write clean, maintainable code with appropriate logging, error handling, and unit/integration tests.
  • Troubleshoot production issues, perform root-cause analysis, and implement preventive fixes.
  • Collaborate with cross-functional teams to refine requirements, plan deliveries, and ensure smooth releases.
  • Document technical designs, data flows, and operational runbooks to support long-term maintainability. Minimum Qualifications:
  • 3–5 years of hands-on experience in software/data engineering roles.
  • Bachelor's or Master's degree: BTECH, MTECH, MCA, MSC.
  • Strong programming experience in Java with solid understanding of OOP and coding best practices.
  • Practical experience with Apache Spark for batch data processing.
  • Experience building and supporting ETL pipelines and data transformations.
  • Working knowledge of DBX for developing and running data workflows. Preferred Qualifications:
  • Experience designing end-to-end data pipelines including ingestion, transformation, validation, and publishing layers.
  • Strong understanding of distributed processing concepts and Spark internals for performance tuning and stability.
  • Exposure to CI/CD practices for data/engineering workflows and disciplined release management.
  • Experience with production monitoring, alerting, and operational support for data pipelines.
  • Proven ability to collaborate with stakeholders, communicate trade-offs, and deliver within timelines.

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