Data Warehousing Architect

The Data Warehousing Architect leads the development of the data warehouse and the execution of data flows to integrate data from multiple sources. This role is responsible for architecting and optimizing the Snowflake data warehouse and ensuring data structures support strategic initiatives across inventory, pricing, purchasing, finance, and network marketing.

Core Competencies & Responsibilities

1. Data Architecture & Engineering

Lead the evaluation, planning, and implementation of new data platforms and frameworks including Snowflake and dbt, ensuring alignment with business needs and scalability goals.

Own the design and evolution of the cloud data warehouse (e.g. Snowflake, Oracle Database Migration Services). Maintain the current operational Oracle data warehouse through the evolution process.

Implement and manage tools for scalable ELT transformations (e.g. dbt). Maintain the current operational Oracle PL/SQL ETL process through the transition.

Evaluate and implement real-time data ingestion and advanced processing tools (e.g. Snowpipe, Snowpark, Amazon Sagemaker Canvas) as part of the modernization roadmap.

Ensure data models are optimized for performance, scalability, and business relevance.

2. Operational Process Support

Collaborate with teams in product management, operations, inventory, pricing, purchasing, compensation, sales, and finance to:

Understand evolving business processes.

Ensure data structures support accurate, near real-time insights.

Provide analytical support for process refinement and automation.

3. Industry-Specific Enablement

Support the Director of Business Analytics and Director of Business Intelligence Engineering in evaluating field-facing and product promotions. Support the business analytics teams in executing internal data analysis projects by providing timely and accurate data.

Build data structures to analyze:

o Sales trends

o Enrollment and retention trends

o Compensation trends, costs, and patterns

o Payout accuracy

o Purchase and compensation fraud detection and management

o Effectiveness of promotional strategies

o Field-leader structures and performance

Operationalize business analysis into repeatable, scalable reporting frameworks.

4. Governance, Quality & Compliance

Establish and enforce data governance policies.

Monitor data quality and implement cleansing strategies using predictive and prescriptive analytics.

Ensure compliance with internal standards and external regulations.

Lead development of the DW naming and governance standards.

5. Collaboration

Collaborate with product, analytics, and IT teams to align data initiatives with business goals.

Communicate effectively with executive stakeholders and translate technical concepts into business value.

Preferred Experience:

Leading data platform migrations or greenfield implementations

Experience with Snowflake, dbt, and Tableau Cloud

Designing scalable data architectures for real-time analytics

Supporting operational and industry-specific processes through data

Excellent communication skills in English

Willingness to flex hours at least one-time a week to provide overlap with the Business Intelligence Engineering team