Growth Marketing Data Analyst

ABOUT THE ROLE:

We are a fast-growing SaaS company running performance campaigns across Google Ads, Meta, LinkedIn, TikTok, and organic channels. We need a rigorous, scientifically-minded Growth Marketing Analyst who owns the full measurement picture — not just one channel. Google Ads is our primary paid channel and your deepest focus, but you will analyze every channel we invest in: paid social, organic search, social media growth, email, and referral. You will apply statistical methods, model SaaS-specific metrics, audit our attribution stack, run experiments, and translate complex multi-channel data into clear decisions that move revenue. This is a high-ownership role at the intersection of data science and growth marketing.

KEY RESPONSIBILITIES:

1. Google Ads — Deep Campaign Analysis [ PRIMARY FOCUS ]

• Own the full analytical layer for all Google Ads campaigns — Search, Performance Max, Display, and YouTube — mapped to the SaaS funnel: click → trial → activation → MQL → SQL → paid conversion.
• Decompose CPA, conversion rate, and ROAS at every funnel stage by cohort, audience segment, keyword cluster, match type, device, geo, and time of day.
• Build keyword efficiency models — flag wasted spend, search term irrelevance, cannibalization between campaigns, and match type leakage.
• Monitor smart bidding performance (Target CPA, Target ROAS, Maximize Conversions) and detect when automation is over-fitting or under-serving the SaaS funnel.
• Produce monthly efficiency reports showing cost-per-SQL and cost-per-ARR — not just platform-reported conversions.
• Apply statistical significance testing to every campaign comparison — never declare a winner without a valid p-value or Bayesian posterior.

2. Paid Social — Meta, LinkedIn & TikTok [ MULTI-CHANNEL ]

• Analyze Meta Ads (Facebook & Instagram), LinkedIn Ads, and TikTok Ads performance against SaaS funnel KPIs — trial signups, MQL volume, and pipeline contribution.
• Compare channel efficiency with a consistent framework: cost-per-trial, cost-per-SQL, and LTV-adjusted ROAS across all paid social platforms.
• Identify audience overlap, creative fatigue signals, and frequency saturation across Meta and TikTok — recommend corrective actions backed by data.
• Analyze LinkedIn performance by targeting dimension — job title, company size, industry, seniority — and connect to CRM pipeline data to assess B2B lead quality.
• Flag discrepancies between platform-reported conversions and CRM-verified pipeline across all paid social channels.

3. Organic Search & SEO Performance [ ORGANIC ]

• Analyze organic search performance using Google Search Console and GA4 — track keyword ranking trends, CTR, impressions, and organic traffic contribution to trial signups.
• Identify SEO content gaps and declining ranking clusters — produce data-driven briefs for the content team backed by traffic opportunity modelling.
• Measure cannibalization between paid search and organic search — identify where Google Ads spend overlaps with strong organic rankings and recommend budget adjustments.
• Track Core Web Vitals and landing page performance metrics and correlate them with organic ranking changes and conversion rate shifts.
• Report on organic vs. paid traffic mix trends and model the revenue impact of shifts in the organic/paid balance over time.

4. Social Media Growth & Organic Social Analytics [ ORGANIC ]

• Track and analyze organic social media performance across LinkedIn, Instagram, Facebook, TikTok, and X — follower growth, reach, engagement rate, and content performance by format and topic.
• Go beyond vanity metrics: connect social media engagement data to website traffic, trial signups, and brand search volume — measure the actual business impact of organic social activity.
• Build a content performance model identifying which post types, topics, and formats drive the highest downstream conversion — give the social team data-backed direction.
• Monitor share of voice and brand mention trends — benchmark community growth against competitors using social listening data.
• Analyze the relationship between organic social momentum and paid social efficiency — identify when strong organic presence lowers paid CPAs.

5. Multi-channel Attribution & Measurement Integrity [ ALL CHANNELS ]

• Own the full multi-channel attribution model — connect every touchpoint (paid search, paid social, organic, email, referral, direct) to revenue outcomes in the CRM.
• Audit the full measurement stack — Google Ads tags, Meta Pixel, LinkedIn Insight Tag, GA4 events, CRM pipeline data, and product analytics — and document every gap.
• Challenge the default attribution model: evaluate last-click, data-driven, linear, and time-decay models and quantify where each one misleads spend decisions.
• Build a unified funnel view from first touch to closed-won revenue, showing each channel's true contribution to pipeline across the full SaaS buyer journey.
• Quantify the gap between platform-reported conversions and CRM-verified revenue across every channel — and build correction factors to adjust for it.

6. SaaS Metrics Modelling — LTV, CAC & Payback Period [ ALL CHANNELS ]

• Build and maintain a cross-channel LTV/CAC model — segment by acquisition channel, plan tier, customer segment, and cohort to understand true efficiency per source.
• Calculate payback period by channel and use it to challenge short-sighted CPA-only optimization — push toward LTV-optimized budget allocation across all channels.
• Track trial-to-paid conversion rates and churn rates by acquisition source — identify which channels bring customers who stay vs. customers who churn fast.
• Produce a monthly cross-channel efficiency scorecard showing cost-per-ARR, LTV/CAC ratio, and payback period for every channel side by side.

7. A/B Testing & Experimentation Across All Channels [ ALL CHANNELS ]

• Design, run, and analyze controlled experiments across ad copy, landing pages, bidding strategies, audience targeting, email sequences, and social content formats.
• Always calculate minimum detectable effect and required sample size before launching any test — no underpowered experiments.
• Apply appropriate statistical methods: frequentist hypothesis testing (t-test, chi-squared) or Bayesian frameworks depending on context and volume.
• Maintain a structured experimentation log with hypotheses, methodology, results, confidence levels, and implemented learnings across every channel.

8. Email & Lifecycle Channel Analytics [ MULTI-CHANNEL ]

• Analyze email marketing performance — open rate, click rate, conversion rate — and connect email touchpoints to trial activations and paid conversions in the CRM.
• Measure the contribution of lifecycle email sequences (onboarding, activation, re-engagement) to trial-to-paid conversion rate improvement.
• Track referral and affiliate channel performance — measure contribution to new trial signups and pipeline alongside other acquisition channels.

9. Data Infrastructure, Dashboards & Tracking Tools [ ALL CHANNELS ]

• Own and audit the full tagging setup across all channels — Google Tag Manager, Meta Pixel, LinkedIn Insight Tag, and GA4 event tracking.
• Build automated data pipelines connecting all channel data into a central data warehouse (BigQuery or equivalent) for unified reporting.
• Design and maintain a live multi-channel performance dashboard giving the team real-time visibility into SaaS funnel metrics across every source.
• Build automated anomaly alerts — CPA spikes, conversion rate drops, organic traffic declines, social engagement drops — so issues are caught within hours, not weeks.
• Specify tracking and tool requirements clearly for engineers and developers — act as the analytical project owner, not just a consumer of data.

10. Team Enablement & Strategic Recommendations [ ALL CHANNELS ]

• Translate complex multi-channel findings into clear, decision-ready briefs for the performance, social media, and content teams — no raw data dumps.
• Act as the analytical checkpoint: any significant budget reallocation, campaign restructure, or channel strategy change must pass your data review first.
• Train the marketing team to interpret data correctly — reduce false-positive optimizations caused by misreading platform metrics.
• Present monthly cross-channel findings and budget recommendations to senior leadership with clear ARR and pipeline impact framing.

REQUIRED QUALIFICATIONS

Education:

• Bachelor's degree in Statistics, Mathematics, Economics, Engineering, Computer Science, or any quantitative field.
• A marketing or business degree is acceptable only if paired with demonstrable quantitative skills and a strong analytical portfolio.

Experience:

• 3+ years of hands-on experience in growth analytics, performance marketing analytics, or a data-science-adjacent marketing role.
• Proven experience analyzing paid campaigns across at least two channels — Google Ads is essential, paid social is a strong plus.
• Demonstrated experience designing and running statistically valid A/B tests and communicating results with rigor.
• Strong understanding of SaaS business models — LTV, CAC, MRR, ARR, churn, NRR, trial-to-paid conversion, and payback period.

TECHNICAL SKILLS

■ Must have:

Google Ads — advanced

SQL — complex queries

Statistics & hypothesis testing

Python or R — analytics

Google Analytics 4

Google Tag Manager

Meta Ads Manager

Google Search Console

LTV / CAC / SaaS metrics

A/B test design

Multi-touch attribution

Excel / Sheets — advanced

■ Strong advantage

LinkedIn Campaign Manager

TikTok Ads Manager

BigQuery

Looker Studio / BI tools

Media Mix Modeling (MMM)

Incrementality testing

Bayesian statistics

CRM — HubSpot / Salesforce

WHAT WE OFFER:

• Real analytical challenge — large-scale multi-channel data and decisions that directly move revenue.
• Competitive salary benchmarked to top-quartile analytics roles in Egypt.
• Direct seat at the table — your recommendations influence significant budget decisions from day one.
• Full tools budget — any analytics software, data tools, or learning resources you need.
• Hybrid working flexibility after the initial onboarding period.
• Fast-growing SaaS environment — high ownership, low bureaucracy, direct revenue impact.