Freelance Data Architect

We’re looking for an experienced Freelance Data Architect to lead the strategic development of our enterprise data platform. This role will guide the evolution of our cloud data warehouse environment by setting architectural standards, optimizing cost and performance, and partnering across teams to enable scalable analytics. You’ll serve as the platform’s lead architect while collaborating closely with a Platform Engineer and a Data Governance Specialist to ensure a secure, well-governed, and cost-efficient data warehouse footprint.

It is an independent contract position requiring ~40 hours of work per week. While this role is flexible and 100% remote, candidates must be located in the United States for consideration. 

Who are we?

The Motley Fool is a purpose-driven financial services company on a mission to make the world smarter, happier, and richer. For 30 years we’ve been helping people make better investment decisions through transparency, education, and Foolish fun. We’re a fast-moving, collaborative team that values high-quality work, curiosity, and initiative. We care deeply about what we do, and we’re driven by the impact our work has on real people’s financial futures.

What does this team do?

The Data Democratization, Governance & Strategy team at The Motley Fool is dedicated to making data accessible and actionable for all business end-users. We specialize in the final stretch of data delivery, transforming warehouse data into easily consumable formats. By working collaboratively with third-party vendors and stakeholders, we build data structures that support seamless reporting and valuable business insights. Our close partnerships with business analysts and data engineering ensures that every team at The Motley Fool can leverage data to drive informed decisions and strategic growth.

What would you do in this role?

• Lead Snowflake platform architecture across U.S. and International instances, ensuring alignment to medallion-layer principles and scalable data modeling.
• Define best practices for data structures and modeling, object naming, and compute utilization.
• Partner with the Data Engineering team to guide schema design, improve performance, and promote consistency.
• Drive cost optimization efforts through query and data structure performance optimization..
• Represent the data warehouse platform in strategic projects, like migration projects and enterprise analytics initiatives.
• Collaborate with a dedicated Platform Engineer to implement and maintain Git-based version control, metadata documentation, and object lifecycle standards.
• Mentor engineers and analysts on data warehouse best practices and performance tuning.
• Participate in governance efforts, architecture reviews, and cross-functional collaboration.

What would you bring to this role? 

• 7+ years in data engineering or architecture roles, including 3+ years of hands-on cloud data warehouse experience - Snowflake preferred.
• Expertise in data modeling, SQL performance tuning, and current architectural principles.
• Strong proficiency in SQL and working knowledge of Python.
• Experience with compute monitoring, cost control, and resource optimization.
• Familiarity with GitOps or CI/CD workflows in a data platform setting.
• A collaborative mindset and the ability to communicate effectively across both technical and business audiences.
• Familiarity with ETL processes and tools, like Airflow, and data pipeline optimization.
• Self-motivated with a proactive approach to identifying and implementing optimizations.

Performance Metrics:

• Cost Optimization - Continue to trim costs by at least 10% each quarter through query tuning, resource configuration and architectural improvements until stabilization is met.
• Platform Efficiency - Ensure key scheduled jobs and queries are optimized for performance, with >90% of data pipeline jobs completing within 10 minutes. Provide architectural guidance and SQL/code optimization support to reduce compute usage and to improve query response times across teams.
• Data Model Standardization - Increase adoption of standardized data models (medallion architecture, naming conventions, column level data types, continued RBAC support) across teams with bi-annual audit adherence of 90%.