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Tier-1 brand, metro location, and common Senior Data Engineer title increase candidate density.
Core Spark, Kafka, and data engineering skills are broadly transferable across industries.
Multiple mandatory technical skills and production experience requirements make screening stringent.
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Design, develop, and maintain large-scale batch and streaming data pipelines using Apache Spark (Scala, PySpark) and Kafka.
Build, optimize, and operate real-time streaming architectures focusing on performance, scalability, fault tolerance, and low latency.
Collaborate on production readiness, capacity planning, monitoring, and incident resolution for critical data platforms, integrating distributed storage systems like Apache Ozone and Ceph.
Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience.
Strong hands-on experience with Apache Spark (Scala, PySpark), Kafka-based streaming, and Apache NiFi in production environments.
Proficient with SQL, Linux, shell scripting, Git, and experience working with distributed/object storage systems.
Work Experience Required: Not explicitly mentioned in the JD.
Deep expertise in building and tuning large-scale, low-latency Spark streaming and batch applications at production scale.
Experienced with Kafka streaming semantics, Spark Structured Streaming, and complex event replay workflows.
Comfortable supporting mission-critical production systems under strict SLAs and collaborating across platform, infrastructure, and operations teams.