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Medium—Tier-1 brand increases competition but seniority and niche streaming skills narrow candidate pool.
Medium—streaming and lakehouse skills transfer across industries but require domain-specific experience.
High—explicit 8–12 years and mandatory Spark, Flink, Kafka, lakehouse experience.
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Enhance and develop big data platform components using open source technologies.
Build and operate Kafka-based streaming applications handling ingestion, filtering, enrichment, and replication.
Develop and optimize data processing jobs with Apache Spark and Flink, manage large datasets using Apache Iceberg, ensure performance and cost efficiency.
8 to 12 years of experience in data engineering and software development.
Proficiency in Java, Scala, or Python programming languages.
Practical experience with Apache Spark, Apache Flink, Kafka, and query engines like Trino/Presto.
Strong SQL skills and understanding of data modeling for analytical workloads.
Experienced in working with complex big data pipelines and streaming platforms at scale.
Skilled at performance tuning and cost optimization in big data environments.
Familiarity with data lake table formats (e.g., Apache Iceberg) and real-time analytics stores (e.g., Apache Pinot, Clickhouse) is advantageous but not mandatory.