Enterprise Data Solution Architect

Role: Enterprise Data Solution Architect

Location: Remote

Role Overview:

The Data Solution Architect (DSA) is responsible for reviewing and assessing existing data architecture and platform components, as well as designing, implementing, and managing robust data solutions. Working in a collaborative team environment, the DSA will assess requirements, define technical data strategies, and deliver effective solutions.

The ideal candidate must have a clear vision for proper data architecture and system design to help drive strategies around improving data ecosystems. Additionally, the DSA should possess a strong ability to explore new tools and successfully pilot Proof of Concepts (POC) and Proof of Values (POV) for innovative new solutions.

Key Responsibilities

Architecture Strategy & Roadmap: Review and assess existing data architecture and platform components. Define potential future data architecture roadmaps that align with business objectives, data integrity, availability, and security.

Technology Evaluation & Cost Optimization: Evaluate current tools, services, and technologies across the data stack. Propose go-forward strategies that align with future needs and optimize costs.

Data Management Frameworks: Design and establish data management frameworks encompassing data ingestion, storage, processing, and retrieval processes to support both operational and analytical needs.

Integration Patterns: Design and establish data integration patterns, including third-party SaaS integrations, covering batch, near real-time, and real-time processing needs.

Tool Selection: Evaluate, select, and advocate for appropriate (and emerging) technologies and tools that directly meet business needs.

Stakeholder Collaboration: Work closely with stakeholders across the organization-including external teams, product managers, and developers-to enable and promote a collaborative data culture.

Requirements Scoping: Discover, analyze, and scope data requirements. Create high-level process models to represent operations for the area under analysis.

Research & Development: Proactively research and share emerging technologies, determining the right solution fitment for the business.

Solution Documentation: Arrive at clear solutions by developing data flow diagrams, process diagrams, use case models, and related technical documentation.

Mentorship: Guide and mentor junior Architects and Data Engineers.

Hands-on Data Modeling: Perform hands-on data modeling for Operational Data Store (ODS) and Data Warehouse applications.

Healthcare Interoperability: Execute mapping from HL7 to Target Data Models, and Target Data Models to FHIR.

Extracts & Framework Design: Develop extensive extract strategies and design reusable data frameworks.

Education

Bachelors or Masters in Information Technology, Computer Science or relevant field.