Growth Marketing Data Analyst

Daftra - دفترة is a job website that connects job seekers with employment opportunities across various industries. The platform focuses on providing a user-friendly interface for individuals to search, apply, and manage their job applications efficiently. While the specific industry of Daftra is not provided, the presence of roles such as Growth Marketing Data Analyst suggests an emphasis on data-driven strategies within marketing and business growth sectors. The website likely supports candidates in finding positions that align with their skills and career goals. By offering a centralized platform for job listings, Daftra aims to streamline the hiring process for employers and enhance the job search experience for applicants. As a digital recruitment hub, it plays a role in connecting talent with opportunities in a competitive market.

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. Show more