Data Warehouse Architect

Job Title: Data Warehouse Architect

Location: Richmond, VA (Onsite)

Yakshna Solutions, Inc., (YSI) is a CMMI Level 3 assessed, ISO 9001, 20000:1, 27001 certified, woman-owned small business enterprises, headquartered in Herndon, VA, USA. YSI provides professional IT solutions and services to business corporations and government organizations. YSI is committed to serving business communities as a leading IT vendor providing innovative, quality, and cost-effective IT business solutions and services.

We offer a competitive benefits package that includes the following: 401(k), health, dental, and vision insurance, life insurance, short-term and long-term disability insurance, paid time off, training, and professional development assistance.

Our Client is seeking a Data Warehouse Architect 2. This position will act in support of the Data Management Team to ingest, clean, and process clinical data. This will include, but not be limited to, the following activities:

• Designing the conceptual and logical frameworks required to support the idea of a "clinical data domain".
• Identifying common data elements between multiple disparate origin source data systems
• Devising and implementing a plan to merge data into a cohesive, longitudinal data framework regardless of origin
• Coordinating data extracts / exports with the origin source data systems
• Defining and implementing data cleansing / processing routines

Required Skills & Experience

• 5+ years of experience in data architecture and data processing, with expertise in designing, implementing, and optimizing enterprise data solutions.
• 3+ years of experience working with Oracle Health (Cerner), supporting healthcare data integration, reporting, and analytics.
• Proficiency in Python (5+ years preferred) for data automation, scripting, and analytics, with experience leveraging Google BigQuery for large-scale data processing.
• Working knowledge of Google Cloud Platform (Google Cloud Platform), including cloud infrastructure and modern data engineering best practices, with experience in scalable cloud-based solutions.