Senior Marketing Operations Analyst

Job description

**Who are we?** Smarsh empowers its customers to manage risk and unleash intelligence in their digital communications. Our growing community of over 6500 organizations in regulated industries counts on Smarsh every day to help them spot compliance, legal or reputational risks in 80+ communication channels before those risks become regulatory fines or headlines. Relentless innovation has fueled our journey to consistent leadership recognition from analysts like Gartner and Forrester, and our sustained, aggressive growth has landed Smarsh in the annual Inc. 5000 list of fastest-growing American companies since 2008. **About the role** Smarsh is looking for a Senior Marketing Analyst to build the data foundation that marketing and go-to-market (GTM) teams run on — and that our AI tools will increasingly depend on. AI assistants and agents can only answer revenue questions reliably when the data model beneath them is clean, governed, and well defined. This role owns that layer for marketing: from source-system connectors and Snowflake, through dbt models and a governed semantic layer, to the dashboards, analysis, and AI-driven insight that leadership uses to make decisions. You'll set the measurement and data roadmap, not just execute against it. You'll partner with Marketing Operations, Demand Generation, Sales/Revenue Operations, Customer Success, and IT/Data Architecture, and you'll be a trusted analytical partner to marketing and GTM leadership on planning, pipeline, and growth strategy. ### How will you contribute? **AI-ready data foundation** - 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. **Data architecture & engineering** - 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. **Measurement, attribution & analytics** - 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. **Strategy, reporting & stakeholder leadership** - 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. **Data quality, governance & enablement** - 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. ### What will you bring? - 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. ### Bonus skills & experience - Experience building or evaluating AI agents or LLM-based assistants that answer business questions over a governed data model, including accuracy testing. - Python or R for statistical analysis, automation, or modeling (e.g., forecasting, propensity, marketing mix or incrementality analysis). - Experience with Marketo Measure (Bizible) at an administrator or model-design level. - Familiarity with the broader GTM data ecosystem: sales engagement and conversation intelligence (e.g., Salesloft, Outreach, Gong), data enrichment and hygiene (e.g., Clay, RingLead), ABM/intent platforms, and product analytics (e.g., Heap, Amplitude). - Experience supporting annual planning, territory design, pipeline coverage modeling, or retention KPIs. - Experience in a regulated industry (financial services, healthcare, or similar) where data accuracy and auditability are especially important. - Bachelor's degree in Analytics, Statistics, Computer Science, Business, Marketing, or a related field, or equivalent practical experience.