About the Role -
Chargebee's marketing engine runs on data — and this role is at the centre of it. As Marketing Data & Analytics Lead, you will be the single point of accountability for how marketing performance is measured, understood, and improved. You will work across every marketing function — Demand Generation, Product Marketing, Web, Field, and Corporate Marketing — to surface the insights that drive better decisions.
This is not a reporting role. It is a diagnostic, investigative, and architectural function. You will own how Chargebee tracks and measures marketing effectiveness from first touch to closed-won revenue, including our attribution model, lead and account scoring, and data architecture across a modern GTM stack.
You will bring a deep curiosity about data, a passion for B2B marketing and sales performance, strong proficiency across our toolstack — and a genuine enthusiasm for using AI to move faster and see further than traditional analytical approaches allow.
Job Description
1. Demand Generation Performance Enhancement
- Own and continuously evolve a daily and weekly marketing performance monitoring framework, surfacing anomalies, trends, and issues before they are raised by leadership
- Build and maintain executive-ready dashboards and reporting packs for the Executive and GTM Leadership consumption
- Go beyond the data read: diagnose why metrics are moving, not just that they are moving
- Partner with Demand Gen, PMM, Web, and Field teams to ensure every function has clear visibility into what is working and what is not
2. Funnel & Attribution Analytics
- Own Chargebee's full-funnel reporting — from Marketing Qualified Lead to Closed-Won Revenue — across all GTM motions (Inbound, Outbound, Partnerships)
- Own and continuously improve the attribution model, Marketing Mix models, Incrementality testing, and unified measurement models using internal techstack to accurately represent the contribution of each channel and campaign
- Build and maintain lead scoring and account scoring models, using both rules-based and machine learning approaches, to maximise MQL-to-pipeline conversion
- Identify conversion leakage across funnel stages and partner with relevant teams to design and test improvements
3. Data Architecture & Metric Design
- Own the marketing data layer in the data warehouse — including schema design, table maintenance, and ensuring data freshness and reliability
- Proactively identify gaps in metric coverage: if a data point is not being captured, design and advocate for the architecture to capture it
- Understand how each system in the GTM stack interacts — from Marketo to Salesforce to Marketo Measure to Gong to Clay — and ensure data flows correctly and consistently across them
- Build training datasets and structured data pipelines to support AI/ML model development and evaluate model performance.
4. AI-Powered Analysis & Automation
- Embed AI tools into everyday analytical workflows — using Claude, ChatGPT, Relevance, N8N, and connected enterprise tooling to accelerate analysis, automate reporting, and generate insight at a pace that traditional methods cannot match
- Build automated reporting pipelines and alert systems that surface performance changes in real time
- Prototype and deploy AI-assisted diagnostic tools for funnel analysis, channel performance review, and executive briefings
- Continuously explore how emerging AI capabilities can be applied to Chargebee's GTM data problems
5. Campaign Analytics & Channel Measurement
- Own digital analytics infrastructure: Google Analytics 4, Google Tag Manager, VWO, and connected tracking layers
- Understand how paid channels — including Google Ads — work mechanically: auction dynamics, Quality Score, bidding strategies, conversion tracking, and audience data flows
- Build and maintain training datasets for paid media optimisation, including negative keyword lists, audience exclusion data, and conversion signal feeds
- Ensure UTM taxonomy, tracking standards, and attribution hygiene are maintained and enforced across all digital channels
- Beyond digital, own campaign performance measurement for field and non-digital marketing activities — including events, tradeshows, webinars, and ABM programmes — developing frameworks to assess ROI and contribution to pipeline where direct attribution is not available
What you will bring
Analytical Mindset
- You chase problem statements — given a metric moving in the wrong direction, you will not stop at it went down. You will pursue the why: was it a data quality issue, a campaign change, a competitive shift, a seasonality effect, a systems error
- You have a spidey sense for when a number looks wrong — you catch anomalies before they reach the executive layer
- You are comfortable with ambiguity. You can work from a problem statement without a detailed brief, designing your own analytical approach to find the answer
Technical Skills
- Advanced SQL — you write complex queries, optimise for performance, and validate output before sharing
- BigQuery — data modelling, schema design, scheduled queries, and integration with BI tools
- Marketo — programme logic, lead lifecycle stages, sync behaviour with Salesforce, and data architecture
- Salesforce — reports, SOQL basics, data model, and how it maps to marketing attribution
- Marketo Measure / Bizible — touchpoint mapping, attribution models, and revenue attribution reporting
- Google Analytics 4 and Google Ads — conversion tracking, audience building, and digital performance measurement
- Tableau — or equivalent BI tooling — for building clear, actionable visual reporting
- Python or equivalent scripting — for automation, data transformation, and AI/ML model support
AI & Tools Proficiency
- You use AI tools — Claude, ChatGPT, and others — as a genuine force multiplier, not a novelty
- Experience with automation and workflow tooling: N8N, Zapier, Clay, or equivalent
- Familiarity with machine learning concepts — classification models, scoring models, regression, and how to build and validate training data
- Exposure to or strong interest in AI SDR, conversational AI, and agentic GTM tooling (1Mind, Qualified, etc.)
Domain Knowledge
- Strong understanding of B2B SaaS GTM motion — how Inbound, Outbound, and Partnership pipelines differ and how to measure each
- Understanding of the full marketing channel mix: paid search, paid social, SEO, email, events, partner — and the right metrics for each
- Ability to connect marketing activity to revenue outcomes and communicate that connection clearly to a non-technical audience
Communication & Presence
- You communicate findings, not just data. You can build a concise, well-structured executive narrative from a complex analytical output
- You are proactive — you raise issues before they are flagged to you, and you propose solutions alongside problems
- You work well with cross-functional stakeholders and can adapt your communication style to engineers, marketers, and sales equally
Nice to have
5+ years of experience in marketing analytics or business intelligence.
Excellent communication and collaboration abilities. Able to interface with non-technical stakeholders (business leaders/marketers).
