AI-Driven Marketing Systems Architect

LeanScale, Inc.

Menlo Park (CA)

On-site

USD 150,000 - 210,000

Full time

14 days+

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Job summary

Snowflake is seeking a Senior Marketing Systems Architect to design and optimize the data and systems foundation of Marketing Operations, spanning Salesforce, Marketo, Snowflake, and related MarTech platforms. This hands-on role focuses on scalable data architectures, reliable integrations, segmentation, and governance to enable AI-powered marketing.

Collaborate with GTM and Data Engineering to deliver technical solutions, improve lifecycle performance, and ensure data quality across regions.

Qualifications

5+ years of experience in Marketing Operations, Marketing Technology, Revenue Operations, or a related technical field.
Deep expertise with Salesforce, Marketo, and enterprise MarTech platforms — you've owned platform architecture, not just campaign execution.
Experience with marketing data architecture, system integrations, APIs, SQL, and troubleshooting complex data flows.
Experience designing segmentation, lifecycle, lead routing, and marketing automation architectures at scale.
Hands-on experience implementing and operating a reverse ETL or CDP platform.
Familiarity with modern data platforms (Snowflake or equivalent), data warehouse-to-MAP sync architectures, and AI-enabled marketing technologies.
Experience partnering with GTM Engineering, Data Engineering, or Product Engineering teams to deliver technical solutions.

Responsibilities

Design and optimize Marketing Operations data architecture across Salesforce, Marketo, Snowflake, Hightouch, Clay, and the broader MarTech ecosystem.
Own the Marketo-to-Snowflake data sync: audit field mappings, identify gaps and redundancies, and partner with Data Engineering to remediate.
Redesign segmentation architecture so audience definitions are consistent, maintainable, and scalable across platforms and regions.
Improve lead routing, audience activation, and operational workflows to strengthen lifecycle performance and marketing data governance.
Lead MarTech stack health and rationalization: assess platform utilization against cost, flag consolidation opportunities, and deliver renewal-readiness recommendations.
Partner with GTM Engineering and Data Engineering to design, validate, and implement scalable technical solutions.
Troubleshoot complex platform, integration, and data quality issues to improve system reliability and operational performance.
Establish technical standards and governance that support future automation and AI capabilities.