Data architect- Ohio

Key Responsibilities: 1. ELT Pipeline Engineering & Automation. a. Pipeline Orchestration: Designs and maintains programmatic, automated ELT pipelines (i.e. SQL, Python, or modern cloud data tools) to process data incrementally through the Medallion stages. b. Performance Tuning: Conducts query and cluster tuning for modern cloud data platforms, ensuring highly optimized compute usage during heavy data transformations and processing. c. Data Migration Execution: Writes robust code to safely migrate legacy databases and files into automated Lakehouse ingestion pipelines. 2. Advanced Data Modeling & Cloud Data Architecture. a. Lakehouse Strategy: Works with IT Architecture staff to design and implement a scalable, secure Medallion Data Architecture across cloud environments. b. Layer Optimization: Establishes standards, schemas, and file formats (i.e. Iceberg, Parquet) for ingestion and data lake processing. c. Analytical Modeling: Designs highly efficient physical and logical data structures within the Gold layer, including modern dimensions, facts, and business entity schemas. d. Schema Evolution: Builds resilient data models capable of handling semi-structured data (JSON, API payloads) alongside structured data, maintaining robust version control of data contracts. e. Semantic Layer Design: Transforms complex business reporting requirements into user-friendly semantic layers that support self-service business intelligence. 3. Develop and document standards/best practices. a. Creates documentation of pipelines, architecture, modeling, and migration efforts. 4. Data migrations, business process monitoring. a. Designs standards for the exchange of data between systems, including data security models 5. Reporting business intelligence. a. Understands business intelligence data needs. b. Defines the data warehouse/business intelligence design. c. Implements data warehouses and data marts. d. Implements business intelligence tools and user interfaces. e. Implements enterprise reporting. f. Implements management dashboards and scorecards. g. Implements analytic applications. h. Replicates and transforms data for business intelligence. 6. Modern Data Governance, Metadata Management & Cataloging a. Metadata Integration: Manages and curates the Alation Data Catalog, ensuring all tables, schemas, and views across the Bronze, Silver, and Gold layers are thoroughly indexed, classified, and tagged. b. Lakehouse Data Lineage: Maps and monitors complex, end-to-end data lineage from source applications down to Gold-layer reporting assets within Alation. c. Data Quality Frameworks: Designs programmatic data validation and testing protocols at the boundary between the Bronze and Silver layers to enforce strict enterprise data stewardship. 7. Mentoring. a. Plans and conducts formal mentorship activities for peers and/or lower-level staff via verbal instruction or technical documentation. Mandatory skills

• 5+ years of hands-on experience designing and implementing enterprise data warehouse, data lake, or Lakehouse solutions
• Expert-level SQL skills, including query optimization, performance tuning, and development of complex analytical datasets
• Experience developing automated ELT pipelines using Python, SQL, and modern cloud data platform technologies
• Hands-on experience implementing and supporting Snowflake-based data engineering, data warehousing, and analytics solutions
• Experience designing and managing Medallion Architecture (Bronze, Silver, Gold) data platforms and associated data processing standards Advanced experience with dimensional data modeling, including fact tables, dimensions, star schemas, and semantic layer design
• Experience integrating structured and semi-structured data sources, including APIs, JSON, and file-based ingestion frameworks
• Hands-on experience administering enterprise data catalog and governance platforms such as Alation
• Experience implementing data governance, metadata management, data lineage, and data quality frameworks
• Experience executing data migration and modernization initiatives
• Ability to define technical standards, best practices, and architecture documentation for enterprise data platforms
• Ability to work independently and lead complex, cross-functional data architecture and engineering initiatives
• Excellent communication skills and ability to collaborate with technical teams, business stakeholders, and executive leadership

• Experience with Snowflake cloud-native architecture, including Snowflake Warehouses, Snowpark, Streams, Tasks, and secure data sharing Familiarity with data modeling (i.e. ER Studio) and modern modeling methodologies
• Experience working in Agile, iterative, or rapidly evolving solution environments
• Experience with enterprise data cataloging, metadata management, and data lineage tools (e.g., Alation)

• Strong work ethic/Time & attendance punctuality
• Experience with dbt

• Bachelor’s Degree in Data Analytics, Computer Science, Information Technology, Computer Engineering, or a closely related field.