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High due to Tier-1 brand, popular mid-level data role, metro location, and broad skill requirements.
Low because core data engineering and big-data cloud platform skills are broadly transferable across industries.
High because many mandatory big-data technologies and platform-specific skills are required despite no years listed.
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Design, implement, deploy, and maintain data marts and ETL processes ensuring reliability, scalability, and performance.
Develop and maintain quality indicators along with monitoring and alerting systems for data assets.
Troubleshoot and document data issues adhering to company standards and continuously improve data systems and processes.
Proven experience working in data engineering domain with strong skills in ETL process definition.
Hands-on experience with Scala (preferred) or PySpark or Java for production-quality code.
Experience in big data technologies including MapR filesystem, Apache Spark, Hadoop, Kafka, and Impala.
University degree in computer science or related field or relevant experience.
Deep expertise with Apache Spark architecture and components and data modeling/data visualization concepts.
Hands-on skills in Microsoft Azure, Azure Databricks, Databricks SQL, and familiarity with Snowflake Data Cloud is a plus.
Experience with both SQL and NoSQL databases, scripting languages (Bash and Python), and build tools like Maven and SBT.