Health Plan Data Engineer

SVAM International Inc. is seeking a Health Plan Sr Data Engineer. The role involves developing and optimizing data pipelines and architectures specifically for health plan data, utilizing extensive experience in data engineering and SQL databases.

Responsibilities:

• 6+ years experience in a Data Engineer or similar role
• Health plan experience required
• Experience with relational SQL and NoSQL databases
• 6+ years working in an Oracle environment and advanced knowledge of PL/SQL
• Must know basic Unix scripting and familiarity with Cron Jobs
• Experience building and optimizing ‘big data’ data pipelines, architectures and data sets, and ETL workflow management tools
• Experience with cloud service environments
• Experience with object-oriented/object function scripting languages
• Experience working with business users to understand their function, processes and goals and incorporate this knowledge into value-added data products
• Experience performing root cause analysis to identify opportunities for improvement
• A successful history of manipulating, processing and extracting value from large disconnected datasets

Requirements:

• 6+ years experience in a Data Engineer or similar role
• Health plan experience required
• Bachelors degree in Computer Science, Statistics, Informatics, Information Systems or another quantitative field, or equivalent work experience
• Experience with relational SQL and NoSQL databases
• 6+ years working in an Oracle environment and advanced knowledge of PL/SQL
• Must know basic Unix scripting and familiarity with Cron Jobs
• Experience building and optimizing ‘big data' data pipelines, architectures and data sets, and ETL workflow management tools
• Experience with cloud service environments
• Experience with object-oriented/object function scripting languages
• Experience working with business users to understand their function, processes and goals and incorporate this knowledge into value-added data products
• Experience performing root cause analysis to identify opportunities for improvement
• A successful history of manipulating, processing and extracting value from large disconnected datasets