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Mid-level generalist Big Data role with broad AWS/Spark requirements increases applicant competition.
Big Data platform skills are transferable across industries but require specific tooling experience, so moderate sensitivity.
Explicit 5+ years and mandatory PySpark, Databricks and AWS toolset create strict technical filters.
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Design and implement Big Data tools and frameworks using technologies such as Apache Spark, Databricks, Delta Tables, and AWS services.
Develop and manage ETL/ELT processes and preprocess disparate data sets using Athena, Glue, and Spark.
Build and maintain cloud platforms and production systems to support company applications and scalable data solutions.
Minimum 5 years of experience as a Big Data Engineer.
Proficiency in Python and PySpark.
In-depth knowledge and hands-on experience with Hadoop ecosystem, Apache Spark, Databricks, Delta Tables, AWS data analytics services (Athena, Glue, EMR, Redshift).
Experience with JSON and Parquet file formats; knowledge of NoSQL and RDBMS databases.
Operator familiar with building and managing large-scale Big Data solutions in cloud environments, especially AWS.
Experienced in collaborating with software development teams to integrate data solutions.
Skilled problem solver with strong project management abilities focused on scalable data architecture.