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 itecting 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
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8 - 10 Years
2 Openings
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