Data Architecture & Design
Define and document reference architectures, design patterns, and standards for the enterprise data platform. Create technical design documentation, data flow diagrams, and architectural decision records (ADRs) while remaining actively involved in hands-on implementation. Establish data modeling standards, naming conventions, and best practices across the platform.
Establish and maintain data modeling standards, design patterns, and architectural guidelines. Review and approve technical designs to ensure alignment with architectural principles and enterprise standards. Collaborate with stakeholders to define data governance policies and ensure compliance with security requirements.
Technical Mentorship & Collaboration
Provide architectural guidance and hands-on mentorship to engineers through code reviews, pair programming, and technical design sessions. Share expertise in data vault modeling, dbt development, and cloud data engineering best practices. Foster a culture of technical excellence and continuous learning within the team. Work collaboratively on our small team where everyone contributes to solving complex technical challenges.
Data Vault Implementation
Design and implement data vault 2.0 modeling patterns to build a scalable, audit-friendly enterprise data platform that supports business agility and data governance.
Build and maintain automated data pipelines using dbt (Cloud/Core), Python, and Snowflake to transform raw data into business-ready datasets with comprehensive data quality testing.
Cloud Data Platform Development
Architect and implement an enterprise data platform on Snowflake, including automated deployment pipelines, data quality frameworks, and monitoring solutions. While we are modernizing to a cloud data platform, on-premises work is still needed using SSIS and MSSQL Server during the migration phase.
Data Mart & Dimensional Modeling
Design and build data marts using dimensional modeling techniques (Kimball methodology) to support business intelligence and analytics requirements.
ETL/ELT Pipeline Development
Design and implement robust data transformation models using dbt, SQL, and Python to build scalable ingestion and processing pipelines.
Implement comprehensive data quality testing frameworks using dbt tests, custom Python validations, and automated monitoring to ensure data accuracy and reliability.
External Data Integration
Integrate and operationalize data from external systems such as CRM, ERP, and third-party platforms via secure cloud data sharing, CDC, and APIs.
Enable reliable, scalable, and automated data workflows by implementing DataOps best practices for continuous integration, testing, deployment, and monitoring across the data pipeline lifecycle. Establish and maintain Snowflake security governance through role-based access control (RBAC), including the design and management of role hierarchies, privilege grants, and object-level permissions to enforce least-privilege principles across all data assets. Define and enforce data access policies for users, service accounts, and downstream consumers by leveraging Snowflake’s virtual warehouses, resource monitors, row-level security, and dynamic data masking to ensure compliant and auditable data access at scale.
Play an integral role in planning, designing, and implementing data migration strategies from legacy on-premises SQL Server systems to our modern Snowflake cloud platform.