Senior Marketing Data Analyst
Abnormal SecurityRemote - USAPosted 30 March 2026
Job Description
About the Role
We have built the engine (Snowflake, Sigma, Marketing Automation Platforms), and now we need you to drive the strategic analysis. Abnormal Security is looking for a Lead Marketing Data Analyst who is part detective, part data architect, and part business consultant. In this role, you will own the system, the signal, and the initial theories regarding performance anomalies. You will not just report numbers; you will uncover the "Why" behind our performance. You will partner with our BI Architects and Revenue Marketing leaders to diagnose funnel friction, pioneer our shift from static dashboards to AI-driven "Ask Anything" agentic analytics, and ensure optimal capital allocation. Crucially, you will provide first-pass diagnoses of data trends so our GTM Strategy Analytics team can make swift, validated business decisions.
What you will do
Strategic Insights Root Cause Analysis
Go beyond "what happened" to uncover "why it happened" by proactively identifying trends in campaign performance, pipeline velocity, and conversion rates.
Own the "First Pass" diagnosis: Identify material variances and provide initial possible theories (e.g., ICP mismatch, routing error, or underperformance of expected MQL results).
Quantify the impact of these trends and propose possible corrective actions (The Math + The Logic) to hand off to GTM Strategy for fiscal validation and final decision (The Business + The Budget).
Partner with the Field, Digital, Strategic Events, Integrated Campaigns Channel Marketing teams to optimize territory-based performance using the new Multi-Touch Attribution (MTA) models.
Data Integrity, Funnel Science Day-to-Day Operations
Maintain reporting hygiene, standard funnel conversion metrics, and recurring dashboards while monitoring performance versus plan.
Act as the "Check Engine Light" for the GTM engine by monitoring data flows between Marketo, Salesforce, 6Sense, and Snowflake to identify silent failures or data quality erosion.
Analyze the "Golden Path" to revenue to determine the optimal mix of touches (digital, event, human) that accelerates a prospect from "Engaged" to "Closed Won".
Validate the output of our AI Agents (e.g., Account Matching, Email Personalization) to ensure automated decisions are accurate.
Architecting and Maintaining the "AI Analyst"
Lead the transition from static reporting to dynamic, AI-driven insights by building and training AI agents to automate L1/L2 data questions.
Build and maintain the semantic layer in Sigma/Snowflake that powers our "Ask Anything" capability, ensuring the AI understands our complex B2B business logic (Buying Stages, 6QA, Deal DNA).
Apply your deep understanding of business logic to train the "Ask Anything" AI Agent so it effectively thinks like a strategist.
Design "agents, data structures and functionality" that not only answer questions but suggest the next logical question to the user.
Stakeholder Partnership
Serve as the primary data consultant for the Revenue Marketing leadership team.
Translate complex data sets into clear, executive-ready narratives, presenting initial points of view to tee up solutions for strategy validation.
Mentor junior team members and educate the broader marketing org on data literacy and self-serve tools.
Own the end-of-quarter MBO target attainment reporting for the Revenue Marketing team (excluding SDRs).
Partner closely with Strategy Analytics (who set the targets), Revenue Marketing Leadership, and the FPA team to validate performance data and ensure accurate, data-backed alignment on bonus payouts.
Must Haves
Experience: 7+ years in Marketing Analytics, BizOps, or Data Science within B2B SaaS.
The “Senior Strategic Partner" Factor: Proven ability to influence executive decisions through data. You don't just supply data; you change minds by exercising "Business Judgment," not just "Data Retrieval".
Technical Stack: Expert-level SQL, deep knowledge of Agent Development, Snowflake / Sigm ... (truncated, view full listing at source)
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