Sr Data Warehouse Architect

Shirley Ryan AbilityLab•Chicago, IL

2d•Hybrid

About The Position

The Senior Enterprise Data Warehouse Architect is responsible for designing, implementing, and maintaining enterprise-level data & database designs, supporting data warehousing and advanced analytical solutions. This role translates strategic and tactical business information requirements into technical data warehouse architecture, implementable data models, and data pipeline strategies. The Senior Enterprise Data Warehouse Architect will consistently demonstrate support of the Shirley Ryan AbilityLab statement of Vision, Mission and Core Values by striving for excellence, contributing to team efforts and showing respect and compassion for patients and their families, fellow employees and all others with whom there is contact at or in the interest of the institute. The Senior Enterprise Data Warehouse Architect will demonstrate Shirley Ryan AbilityLab Core Attributes: Communication, Accountability, Flexibility/Adaptability, Judgment/Problem Solving, Customer Service and Core Values (Hope, Compassion, Discovery, Collaboration, & Commitment to Excellence) while fulfilling job duties. The Senior Enterprise Data Warehouse Architect will: Design & Architecture Responsible for the data architecture and database design of the Enterprise Data Warehouse, collaborating with other Data platform architects on the overall architecture of the data warehouse ecosystem. Create logical and physical data models to support analytical needs of the organization, guide and oversee data modeling activities executed at a project or program level Define data standards, naming conventions, and data architecture best practices. Data Management & Optimization Ensure data architecture supports all Data Sets (types), and maintains integrity across systems. Optimize data storage, indexing, and query performance for analytical workloads. Governance, Security & Compliance Collaborate with Data Governance team to Implement data policies, including metadata management and data lineage tracking. Ensure compliance with security, privacy, and regulatory requirements (e.g., GDPR, HIPAA depending on industry). Collaboration & Stakeholder Alignment Work closely with business analysts, data engineers, data scientists, and leadership to translate business needs into technical solutions. Communicate architectural decisions and provide guidance to development teams. Technology Evaluation & Innovation Collaborate in the evaluation, testing, and recommendation of new tools, platforms, and technologies (cloud data warehouses, BI tools, ETL & MLL frameworks, etc.) Stay current with trends in data architecture, cloud platforms, and big data technologies. Performance Monitoring & Troubleshooting Monitor data warehouse performance and resolve bottlenecks and/or data quality issues. Ensure Data design reflects high availability, reliability, and scalability of the data warehouse environment. Reporting Relationships Reports directly to the Director of Data Platform Architecture

Requirements

Bachelors or Masters degree in Computer Science, Information Technology, or relevant field.
8+ years of proven experience as an Enterprise Data Warehouse Architect role, preferably in an environment integrating data from medical EHR systems into Data warehouse / BI platforms
8+ years of experience (as DBA or Data Warehouse Architect) with Snowflake and/or Oracle Autonomous Database (ADB)
8 years of experience creating / leading data modeling activities for Data Warehouse or analytical solutions
In-depth understanding of database management systems (SQL & NOSQL) , online analytical processing (OLAP), and ETL (Extract, transform, load) frameworks.
Experience in data warehouse design (e.g. dimensional modeling), supporting multiple analytics outcomes (include Data Mining, AI and MLL)
Experience with modern BI / Information delivery technologies (e.g. Microsoft Power BI, Oracle OAS, Tableau Cloud).
Experience with modern Analytical Data management platforms (Snowflake, Databricks, Azure Fabric, etc..)
Familiar with common Data Warehouse architecture design strategies and appropriate application; including medallion, data Lake, data mart, ODS, etc
Experience integrating data from medical EHR systems, and Workday financial SAAS platform into Data warehouse / BI platforms
Working knowledge of SQL and NLQ data access techniques and methodologies
Excellent organizational and problem-solving abilities.
Experience leading analytical development programs

Responsibilities

Create and maintain physical databases, dimensional data models, schemas, and data dictionary documentation.
Guide and oversee data modeling process, technologies, and outcomes
Ensure database design(s) and implementation support strategic information needs of the organization, and maintain the Data Warehouse Data Roadmap
Optimize data warehouse performance, perform data profiling, and ensure data quality, integrity, and security across platforms.
Work with Business Analysis, EDW platform Engineering, and IT teams to deliver high-performance analytics capabilities.
Create logical and physical data models to support analytical needs of the organization, guide and oversee data modeling activities executed at a project or program level
Define data standards, naming conventions, and data architecture best practices.
Ensure data architecture supports all Data Sets (types), and maintains integrity across systems.
Optimize data storage, indexing, and query performance for analytical workloads.
Collaborate with Data Governance team to Implement data policies, including metadata management and data lineage tracking.
Ensure compliance with security, privacy, and regulatory requirements (e.g., GDPR, HIPAA depending on industry).
Work closely with business analysts, data engineers, data scientists, and leadership to translate business needs into technical solutions.
Communicate architectural decisions and provide guidance to development teams.
Collaborate in the evaluation, testing, and recommendation of new tools, platforms, and technologies (cloud data warehouses, BI tools, ETL & MLL frameworks, etc.)
Stay current with trends in data architecture, cloud platforms, and big data technologies.
Monitor data warehouse performance and resolve bottlenecks and/or data quality issues.
Ensure Data design reflects high availability, reliability, and scalability of the data warehouse environment.