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Spark

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Onsite

Spark

Infosys

Spark

Skills

Big DataData EngineeringKafkaApache AirflowHadoop

22 days ago

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Important information


Contract type:

Permanent

Location:

India

Work mode:

Onsite

Published on:

28 September 2026

What they need


  • Primary skills:Technology->Big Data - Data Processing->Spark

Key Responsibilities:

  • Design, develop, and maintain scalable data processing jobs using Spark for batch and/or near-real-time workloads.
  • Analyze large datasets to identify trends, anomalies, and data quality issues; implement validation and reconciliation checks.
  • Optimize Spark applications for performance by tuning partitions, caching strategies, memory usage, and execution plans.
  • Collaborate with cross-functional teams to translate business requirements into technical solutions and well-defined deliverables.
  • Implement robust error handling, logging, and monitoring to ensure reliability and easier troubleshooting.
  • Participate in code reviews, follow engineering best practices, and contribute to reusable components and standards.
  • Support deployments and production issues by performing root-cause analysis and implementing preventive fixes. Minimum Qualifications:
  • Bachelor’s degree (or equivalent) in Engineering/Technology/Computer Science or related field (BTech/BE/MSc or equivalent).
  • 3–5 years of experience working on data engineering or big data processing initiatives.
  • Hands-on experience building and maintaining Spark-based data processing solutions.
  • Strong understanding of distributed computing concepts and data processing fundamentals.
  • Ability to work independently on assigned modules and collaborate effectively within a team. Preferred Qualifications:
  • Master’s degree (MTech/MCA or equivalent) in a relevant discipline.
  • Proven experience delivering end-to-end Spark pipelines, including development, testing, and production support.
  • Experience improving job performance and stability through Spark tuning and structured troubleshooting practices.
  • Familiarity with building reusable frameworks/components to standardize Spark development across projects.
  • Strong communication skills to explain technical trade-offs and align solutions with stakeholder expectations. Good to have skills: Hadoop, Hive, Kafka, Airflow, Delta Lake

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