Marketing Data & Analytics Lead

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).