Develop and deliver long-term strategic goals for data architecture vision and standards in conjunction with data users, clients, and other key stakeholders.
Lead data architecture, data modeling, and data movement initiatives to enhance the architecture of the data warehouse which supports analytics for Medicaid and Medicare and Commercial healthcare data; including domains such as claims, provider, TPL, and member information subject areas.
Evaluating existing data systems. Working with the development team to create conceptual data models and data flows.
Ensure that data strategies and architectures are in regulatory compliance. Recognize and identify potential areas where existing policies and procedures require change, or where new ones are needed.
Develop and implement key components as needed to create testing criteria to guarantee the fidelity and performance of data architecture.
Document data architecture and enterprise environment to maintain a current and accurate view of the larger data picture, an environment that supports a single version of the truth and is scalable to support future analytical needs.
Create and maintain data model and metadata policies and procedures for functional design.
Required Experience/Skills
Expertise in Data Architecture, Data Strategy, and Roadmap for large and complex health entities and systems and implemented large-scale end-to-end Data Management & Analytics solutions for more than one large client.
Strong knowledge of regulatory security requirements regarding HIPPA, PHI, PII, and other healthcare data security standards.
Strong Healthcare domain knowledge of Medicaid, Medicare, and Commercial healthcare data sets.
Expertise with normalized OLTP, OLAP, MDM Dimensional modeling techniques, Star schemas, slowly changing dimensions, role-playing dimensions, dimensional hierarchies, and data classification.
Expert-level SQL skills with direct experience writing complex analytic queries, stored procedures, DDL, DML, and DCL. Thorough knowledge of query optimization techniques with the ability to write code to ANSI standards and inspect code written by other developers.
Expertise in Data Quality, Data Profiling, Data Governance, Data Security, Metadata Management, MDM, Data Archival and Data Migration strategies using appropriate tools.
Experience with scripting language used for analytics such as Python or R.