Principal Data Analyst, Marketing Analytics
- Airalo
- Remote Spain
Job Description
Do you thrive at the intersection of marketing science and business strategy? We're looking for a Principal Data Analyst, Marketing Analytics to own and execute our marketing measurement strategy - from Marketing Mix Modeling and incrementality testing through to attribution, channel economics, and budget allocation.
This role is for someone who combines deep technical expertise with the ability to influence how a global organisation thinks about growth. You'll design experiments, build models, and deliver the insights that shape how we invest our marketing spend across 190+ countries. But measurement only matters if it changes decisions - so you'll also drive adoption of your work across the Growth organisation, build measurement literacy with stakeholders, and elevate the data culture that turns analysis into action.
If you're ready to operate at the frontier of modern marketing measurement in a high-growth, global business - we'd love to hear from you.
Responsibilities include, but are not limited to:
Manage and evolve Airalo’s growth MMM portfolio - driving the existing market model from validation into a production-grade decision tool, and scaling to additional markets as growth ambition and data readiness allow.
Design and execute incrementality experiments (geo-holdouts, conversion lift studies, synthetic control, difference-in-differences) that calibrate the MMM and establish true causal impact of marketing spend across channels.
Evolve our attribution methodology: determine the right models and attribution windows for our purchase cycle, specify the data requirements, and measure the impact of tracking remediation on attribution accuracy.
Calculate and continuously optimize the CAC metrics (platform-reported, internally-attributed, incremental, and blended) and own LTV:CAC as a strategic KPI reported to leadership.
Build measurement literacy within the Growth and Acquisition teams: train stakeholders to interpret the LTV/CAC related metrics, understand the difference between attributed and incremental performance, and use self-service reporting with confidence.
Build and maintain the performance marketing reporting framework - with Analytics Engineering to ensure the underlying models serve both reporting and measurement needs.
Act as analytics partner to the Growth and Acquisition teams: translate business questions into measurement plans, deliver the analytics that inform spend decisions, and build self-service reporting that reduces ad-hoc dependency.
Drive adoption of measurement outputs-ensure MMM scenarios, incrementality results, and attribution insights translate into concrete budget allocation changes
Contribute to the development of a unified decision framework that integrates signal health, attribution, and incrementality into budget allocation guidance with clear go/no-go criteria for scaling spend by market and channel.
Build institutional knowledge: document every experiment result, every MMM refresh, and every signal quality trend so that each quarter’s decisions are better informed than the last.
Collaborate with the Senior CDP Engineer and MarTech on the data and signal infrastructure that underpins measurement - defining what you need, so they can build it right.
Must-haves:
Several years of experience in marketing analytics, marketing science, or growth analytics, with deep expertise across at least two of Marketing Mix Modeling, Incrementality Testing (geo-experiments, RCTs), and Multi-Touch Attribution.
Hands-on experience building, validating, or calibrating MMM models- whether using Robyn, Google Meridian, PyMC-Marketing, LightweightMMM, Bayesian regression, or working closely with vendors who do.
Strong foundation in causal inference and experimental design: you understand difference-in-differences, synthetic control, propensity scoring, and when each method is appropriate.
Expert-level SQL and Python (or R). You can write production-quality code, not just analysis notebooks.
Experience with modern data warehouses (BigQuery or Snowflake) and familiarity with analytics engineering workflows (dbt preferred).
Experience with data visualisation and BI tools such as LightDash, Looker Studio, Tableau, or Metabase.
Proven track record of calculating and optimising channel-level CAC, LTV, churn, and ROAS, and using these metrics to influence marketing spend decisions at scale.
Exceptional communicator: you navigate deep technical conversations with data scientists and translate findings into clear recommendations for senior stakeholders and board-level audiences.
A proactive, self-starter mindset. You thrive in ambiguity, work autonomously, and are energised by building in fast-paced, high-growth environments.
Nice to haves:
Experience with Bayesian modelling frameworks (TensorFlow Probability, PyMC, Stan) and their applications in marketing measurement.
Familiarity with mobile analytics platforms and MMPs: Adjust, AppsFlyer, CleverTap, or similar.
Experience with ad platforms (Google Ads, Meta Ads, TikTok Ads, Apple Search Ads) and their attribution APIs, conversion modelling, and server-side event integration (cAPI, Enhanced Conversions, SKAN).
Knowledge of the eSIM, telco, MNO/MVNO, or travel-tech landscape.
Exposure to semantic layers, metrics-as-code, or KPI governance frameworks.
Experience with privacy-first measurement strategies in the post-cookie, post-ATT world.
Experience with cross-border or multi-market attribution challenges where marketing geography and conversion geography diverge.
