Senior Marketing Operations Analyst, Smarsh

• 5+ years of experience in GTM, marketing, or revenue operations analytics, or business intelligence, ideally in B2B SaaS or technology
• Advanced SQL and hands-on experience with Snowflake or a comparable cloud data warehouse (e.g., BigQuery, Redshift, Databricks), including designing data models, not just querying them
• Hands-on dbt experience building production-grade, tested, documented models
• Experience building or governing a semantic or metrics layer (e.g., dbt Semantic Layer, LookML, Omni, or a BI-native equivalent)
• Experience with ELT and orchestration tooling (e.g., Fivetran, Airflow, or similar)
• Deep working knowledge of Salesforce data structures (leads, contacts, accounts, opportunities, campaigns, campaign members) and Marketo or a comparable marketing automation platform
• Expert BI skills in at least one major platform (e.g., Power BI, Looker, Tableau, Omni, Hex), including governed, self-serve dashboard design
• Proven experience designing and governing multi-touch attribution and ABM reporting, and explaining methodology and its limits to non-technical executives
• Practical, hands-on use of AI tools (e.g., Claude, Claude Code) in analytics work, and a clear point of view on what makes data AI-ready
• Track record of owning an analytics roadmap and influencing senior stakeholders with data
• Excellent written and verbal communication; able to turn complex analysis into a concise business narrative

Desirable

• Design and build an AI-ready data foundation for marketing and GTM: governed dbt models and a semantic layer (e.g., dbt Semantic Layer, LookML, Omni) with consistent metric definitions that both people and AI tools can query
• Prioritize foundation domains in partnership with RevOps and IT — starting with marketing performance and attribution, and connecting to pipeline, account hierarchy, and customer retention data
• Build validation and testing frameworks (data tests, reconciliation checks, and accuracy evaluation of AI-generated answers) so AI assistants and agents return answers leadership can trust
• Partner with Marketing Operations and IT to deploy and improve AI tools (e.g., Claude, BI-native AI agents) that answer recurring marketing and pipeline questions in a self-serve way
• Own the marketing data architecture end to end: source-system connectors, ingestion into Snowflake, transformation logic, orchestration, and curated data models
• Build and maintain reliable pipelines (e.g., Salesforce, Marketo, Marketo Measure/Bizible, ABM, Google/LinkedIn/Bing Ads, sales engagement platforms) using ELT and orchestration tooling such as Fivetran and Airflow, with monitoring and documented SLAs
• Model account hierarchies and lead-to-account matching so marketing engagement rolls up correctly to accounts, buying groups, and opportunities
• Own the design, governance, and evolution of marketing attribution (W-Shaped, Custom Model, and account-level influence), including documenting assumptions and limitations
• Lead ABM measurement: account engagement scoring, buying-group coverage, intent-to-pipeline conversion, and target-account progression
• Go beyond descriptive reporting — apply cohort analysis, funnel velocity, incrementality/test-and-control design, and forecasting to answer "what's working, and what should we do next?"
• Analyze campaign and channel performance (organic search, paid search, paid social/ABM, display, email, events, field) and deliver prioritized recommendations on budget and program mix
• Partner with marketing and GTM leadership on annual planning, pipeline targets and coverage, growth strategy, and operating cadences, bringing the analysis that grounds those decisions
• Build and own executive-grade dashboards (e.g., Power BI, Looker, Tableau, Omni) reconciled against Salesforce and finance data, and own the analytics behind monthly/quarterly business reviews and QBRs
• Define and govern a single set of marketing metric definitions (MQL/MQA, SAL, pipeline sourced vs. influenced, CAC, ROI) and drive adoption across Marketing, Sales, and Customer Success
• Set and maintain the marketing analytics roadmap: prioritize requests, balance ad hoc work against foundational builds, and communicate trade-offs
• Establish data quality standards and automated checks across CRM, marketing automation, and the warehouse; fix root causes rather than patching symptoms
• Ensure marketing data and AI use align with Smarsh data 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 of AI in analytics