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Popular Data Engineer title and early-mid experience raise applicant density despite non-metro location.
Skills like PySpark, Snowflake, and cloud are highly transferable across industries.
Explicit 1-4 years plus many mandatory big-data skills enforces strict technical filters.
Design, develop, and maintain scalable ETL/ELT data pipelines and data platforms leveraging big data technologies.
Build and optimize data warehouses using Snowflake and implement workflow orchestration via Airflow or equivalent tools.
Ensure data quality, validation, and performance optimization across cloud platforms like AWS, Azure, or GCP.
1-4 years of work experience in data engineering or related roles.
Hands-on experience with Hadoop ecosystem (HDFS, YARN, MapReduce), Apache Spark (PySpark, Spark SQL), and Snowflake data warehousing.
Proficient in advanced SQL, Python (including PySpark), and ETL/ELT pipeline development.
Experience working with cloud platforms such as AWS, Azure, or GCP and workflow orchestration tools like Apache Airflow.
Technical proficiency with large-scale distributed data systems and cloud-native services across at least one major cloud provider.
Experience with data modeling techniques including Star and Snowflake schemas and slowly changing dimensions (SCD Types).
Demonstrated ability to collaborate with cross-functional teams including data analysts and scientists while focusing on data quality and cost/performance optimization.