Snowflake Data Architect (LOCAL CANDIDATES - DALLAS, TX)

POSITION TYPE: Contract (5 months - Possible contract to hire)

Position Overview: 14 years + Experience in Snowflake, SQL, AWS Data (Sage maker, Glue, Kinesis) and Cloud Data Engineering. As a Snowflake Data Architect (EA), you will be the strategic glue between business goals and IT execution for all Data engineering efforts. Your role is to design, implement, and optimize a high-performance data warehouse environment (Snowflake, Databricks, AWS) that serves as the "single source of truth." You won't just be moving data around; you'll be architecting a scalable foundation that enables real-time analytics, data sharing, and AI-driven insights across the enterprise. Your mission is to ensure that our technology data stack is scalable, secure, and-most importantly- aligned with where the company wants to be in five years.

Key Responsibilities: Architecture Design: Design end-to-end data architectures within Snowflake, utilizing Virtual Warehouses, multi-cluster environments, and specialized storage optimization. Data Modeling: Create and maintain complex data models (Star Schema, Snowflake Schema, Data Vault 2.0) tailored for cloud-native performance. Strong Collaboration with various IT and Business stakeholders to curate, document and implement Data lineage, knowledge graphs, Data tracing and Compliance KPIs. Pipeline Engineering: Architect robust ETL/ELT pipelines using Snowpipe, Tasks, and Streams, integrating with tools like airbyte, dbt, Fivetran, or AWS.Governance & Security: Implement Role-Based Access Control (RBAC), data masking, and row-level security to ensure compliance with global standards (GDPR, HIPAA, etc.).Performance Tuning: Monitor and optimize query performance and credit consumption- ensuring the system is as cost-effective as it is fast. Expert-level SQL and deep knowledge of Snowflake-specific features (Time Travel, Zero-copy Cloning, Data Sharing etc. Snowflake Reference Designs: Implement reference designs for ETL/ELT pipeline engineering including integration efforts with AWS platforms. Data Governance: Setup architecture and governance standards from Data engineering and Platform perspective and enforce Data Lineage, Data Stewardship across all business units.