Responsibilities

Analyzing existing data sources Expert understanding of data models and various 1 to many, many to many and other patterns, normalization and denormalization patterns and purposes Profile data sources to reverse engineer data model and relationships between tables, identify key fields, and infer meaning of attributes Meet with system owners to tie observations of data patterns with business processes and use cases that lead to those patterns Conduct root cause analysis (RCA) to identify underlying issues and drive effective solutions. Perform frequency distribution analysis to identify patterns and trends in the data.Architecting data strategy for Future State Profile the data sources to identify anomalies, inconsistencies, and data quality issues. Take ownership of the data, ensuring accuracy, completeness, and reliability. Develop target data model that provides optimal long term functional opportunities Design the right change data capture, audit strategy Identify where reference tables are necessary Design mapping tables / helper tables to support configurable ETL

Requirements:

General Attributes Advanced sql skills, familiarity with a variety of DB technologies (oracle, sql server, etc) Comfortable with AWS environment and databricks Experience with ETL Demonstrate curiosity and a relentless pursuit of understanding complex datasets. Engage with various stakeholders, including clinicians, researchers, data scientists, and IT professionals, to gather requirements and ensure alignment. Communicate effectively with stakeholders at all levels, translating technical concepts into clear and actionable insights. Lead deep data analysis initiatives to extract meaningful insights and drive data-driven decision-making Collaborate with cross-functional teams to develop and implement data models and solutions that meet business objectives