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Popular mid-level Data Engineer with broad cloud and big-data requirements increases candidate competition.
Core cloud, Spark, and ETL skills transfer across industries, but AEP and Scala preference moderately narrows fit.
Multiple explicit technology requirements and a 6+ year experience minimum make shortlisting highly strict.
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Design, build, and maintain scalable data pipelines using cloud technologies and big data tools.
Develop and train machine learning models using Python and Azure to generate business insights.
Collaborate with product development to resolve data-related issues and ensure data quality with checkpoints.
5 to 9 years total experience; specifically 6+ years with Google Cloud Platform, Spark, and Scala.
Bachelor's or Master's degree in Computer Science or a related technical field.
Proficiency in Python, SQL, RDBMS (preferably SQL Server), and scripting languages including Shell, C#, and Java.
Experience with cloud platforms AWS and Google Cloud services (S3, Redshift, EMR), containerization tools Docker and Kubernetes, and SQL optimization tools like TOAD and Oracle SQL Developer.
Experienced in scalable data engineering within cloud ecosystems, especially Google Cloud and AWS.
Proficient in multi-language scripting and automation, with strong Python OOP skills.
Skilled in big data ecosystem tools, data modeling (Kimball), data governance, and integrating ETL and data visualization tools.