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Tier-1 brand plus a popular mid-level Data Engineer title and 4–8 years experience increases applicant competition.
Core data engineering skills are transferable across industries, but streaming and lakehouse specialization raises domain specificity.
Explicit 4–8 years plus mandatory Spark/Flink/Kafka/Trino/Iceberg and strong SQL makes filters stringent.
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Develop and enhance big data platform components focusing on open-source technologies such as Apache Spark, Flink, Kafka, and Iceberg.
Build, operate, and optimize Kafka-based streaming applications for ingestion, filtering, enrichment, and replication workloads.
Tune data processing jobs for performance, scalability, and cost efficiency while maintaining clean and testable code, participating in code reviews.
4–8 years of experience in data engineering and software development.
Proficient in high-quality coding with Java/Scala, Python, or equivalent 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 building and scaling high-volume streaming applications and analytical data pipelines using open-source big data tools.
Skilled at performance tuning and cost optimization of data processing jobs in production environments.
Familiarity with data lake technologies and modern streaming analytics ecosystems to support scalable, low-latency analytical queries.