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Mid-density: specific big-data skillset but common data-engineer title and reasonable employer recognition increase competition.
Low because Spark/Hadoop/cloud data engineering skills are broadly transferable across industries.
High due to many mandatory technical skills, cloud migration experience, and leadership responsibilities required.
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Lead design and architecture of scalable big data solutions using Apache Spark and Hadoop ecosystem technologies.
Own end-to-end development, optimization, and tuning of batch and streaming data pipelines including Spark Structured Streaming and Kafka.
Provide technical leadership, conduct code and design reviews, mentor team members, and support production operations and incident resolution.
Strong experience with Apache Spark (Spark SQL, DataFrames, Datasets, Structured Streaming) and Hadoop ecosystem tools (HDFS, Hive, HBase, YARN).
Proficiency in programming languages such as Python, Scala, or Java.
Experience with cloud-based big data platforms like AWS EMR/Glue, Azure Databricks/Synapse, or GCP Dataproc.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in deploying and migrating big data workloads to cloud platforms with knowledge of cloud security and cost optimization.
Skilled in performance tuning and data governance including data quality, security, and compliance in big data environments.
Operates effectively in leadership roles involving architecture design, mentoring, and cross-functional collaboration with architects and product owners.