Senior Marketing Operations Analyst

About the role

Data foundation and engineering

• Build governed dbt models and a semantic layer with consistent metrics that people and AI tools can query.
• Prioritize marketing performance and attribution data, connecting it with pipeline, account hierarchy, and customer retention data.
• Create validation, reconciliation, and AI-answer accuracy checks; help deploy self-service AI tools.
• Own source connectors, Snowflake ingestion, transformations, orchestration, and curated models.
• Build and maintain pipelines from platforms such as Salesforce, Marketo, advertising channels, and sales engagement systems; monitor them and document SLAs.
• Model account hierarchies and lead-to-account matching for accurate account, buying-group, and opportunity reporting.

Measurement and leadership

• Govern W-Shaped, custom, and account-level marketing attribution and document its assumptions and limitations.
• Lead ABM measurement, including engagement scoring, buying-group coverage, intent-to-pipeline conversion, and target-account progression.
• Use cohort analysis, funnel velocity, incrementality testing, and forecasting to identify effective programs and recommend next steps.
• Analyze organic and paid search, paid social and ABM, display, email, events, and field campaigns; recommend budget and program mix.
• Advise leadership on annual planning, pipeline targets and coverage, growth strategy, and operating cadences.
• Build executive dashboards reconciled against Salesforce and finance data and own analytics for monthly and quarterly business reviews.
• Govern definitions for MQL/MQA, SAL, sourced versus influenced pipeline, CAC, and ROI; drive adoption across teams.
• Set the analytics roadmap, prioritize requests, balance immediate analysis with foundation work, and communicate trade-offs.

Data quality, governance, and enablement

• Establish data-quality standards and automated checks across CRM, marketing automation, and the warehouse; address root causes.
• Ensure marketing data and AI use meet privacy, security, and governance requirements in partnership with Legal, Security, and IT.
• Mentor analysts and Marketing Operations teammates on SQL, dbt, data modeling, and responsible use.

Tools and platforms

• Named or example tools include Snowflake, dbt, Fivetran, Airflow, Salesforce, Marketo, Marketo Measure/Bizible, and Google, LinkedIn, and Bing Ads.
• Examples of semantic-layer or BI tools include dbt Semantic Layer, LookML, Omni, Power BI, Looker, and Tableau.
• AI examples include Claude and BI-native AI agents.

Compensation

• Annual salary range: USD 106,000–141,000.

Workplace

• Remote role in the United States.