Hi W***m, I’m J***e, an HR recruiter at StrategyBrain. I came across your profile and was impressed by your e-commerce analytics expertise,from your roles at K***L and C***e to leading insights at A***s. We’re partnering with S***s on a Senior E-commerce Analytics Manager role, where you’d build end-to-end analytics frameworks, own KPI dashboards and A/B testing roadmaps, and mentor junior analysts. Would you be open to a brief chat to explore this opportunity?

Hi W***m, Great to hear you’re interested! I’m available for a quick call on: • Tuesday, April 16th at 10:00 AM CET • Wednesday, April 17th at 2:00 PM CET Do either of these times work for you? If not, feel free to propose an alternative window and I’ll do my best to accommodate. Looking forward to diving into the S***s opportunity,and learning more about what you’re looking for in your next role. Best regards, J***e HR Recruiter, StrategyBrain

Hi W***m, Fantastic,Wednesday the 17th at 2:00 PM CET is locked in. I’ll send a calendar invite with Z***m details shortly. Ahead of our call, here’s a quick overview of the Senior E-commerce Analytics Manager role at S***s and how it lines up with your interests: Company & Culture • S***s is a fast-growing, 200-person e-commerce analytics SaaS firm that puts data at the heart of every decision. We pride ourselves on an innovative, collaborative culture and invest heavily in continuous learning. Role Highlights 1. End-to-End A/B Testing Ownership , You’ll lead the full lifecycle of experimentation: roadmap design, test execution, statistical analysis, and actionable recommendations. 2. Mentorship & Team Leadership , You’ll coach a small team of junior analysts, establish best practices for testing and analysis, and foster a data-driven mindset across the organization. 3. Analytics Frameworks & Dashboards , Build and maintain KPI dashboards, forecasting models, and customer segmentation tools to drive conversion and retention improvements. 4. Cross-Functional Impact , Work closely with marketing, product, and engineering to optimize campaign spend, user journeys, and site performance. Growth & Support • Career Path: Clear progression from manager to senior leadership based on impact and team growth. • Resources: Access to training budgets, analytics tooling, and regular internal workshops. • Compensation: Competitive salary, performance bonus, and equity options. I’m looking forward to diving deeper into how S***s empowers end-to-end ownership and structured mentorship. Feel free to let me know if there’s anything specific you’d like me to cover on the call. See you Wednesday! Best, J***e HR Recruiter, StrategyBrain

Hi W***m, Great questions. 1. Reporting line & team structure , You’ll report directly into the Head of Data & Analytics, who sits on S***s’s senior leadership team. , Right now, the core analytics group you’ll mentor is three full-time analysts: two junior team members (1,2 years’ experience) and one mid-level analyst (3,4 years). As the function grows, you’ll also influence a broader pod of data engineers and marketing analysts. 2. Early-stage challenges , Scaling our A/B testing framework across multiple retail verticals and making experimentation repeatable and reliable , Enhancing our KPI dashboards and forecasting models so stakeholders get clearer, near-real-time insights , Driving adoption of data-driven decision-making across marketing and product teams , Upskilling the junior analysts: establishing best practices, improving statistical rigor, and empowering them to deliver high-impact recommendations quickly Let me know if you’d like any more detail on the team, reporting structure, or initial priorities ahead of our call. Looking forward to diving in on Wednesday! Best, J***e HR Recruiter, StrategyBrain

Hi W***m, Great questions. Here’s a high-level view of our setup: 1. A/B Testing Platform • We run experiments on a cloud-based, enterprise-grade solution that’s fully integrated with our data warehouse and front-end stack. This gives us granular event-level tracking, automated power-and-sample calculations, and a streamlined workflow for rollout and rollback. • Behind the scenes, we leverage our Python/R libraries for advanced statistical checks and to automate post-test analysis. 2. BI & Dashboarding • Our dashboards live on top of a cloud data warehouse and are delivered via a leading BI tool, giving real-time slices on traffic, conversions, channel performance and cohort metrics. • We layer SQL-based models (via d***t) to ensure consistency, then surface insights through interactive dashboards that stakeholders can self-serve. 3. Defining Success Metrics • Early experiments: We co-define primary metrics (e.g. lift in conversion rate, add-to-cart rate or average order value) and secondary “health” metrics (page load time, bounce rate). We target a 95% confidence level and a minimum detectable effect size aligned with business impact. • Forecasting models: Success is two-fold,statistical accuracy (tracked via MAPE/RMSE against actuals) and decision-making impact (e.g. budget reallocation efficiency, reduction in forecast bias). We aim for error margins that keep stakeholders confident in weekly/monthly revenue projections. Hope this gives you a clear picture,happy to unpack any of these points further on our call. Looking forward to diving deeper on Wednesday! Best, J***e

Hi W***m, Great questions,here’s a quick rundown of our current stack: 1. Cloud Data Warehouse & BI • Warehouse: S***e (we isolate dev/staging/prod in separate schemas for safety and performance) • BI: L***r is our primary visualization layer (self-serve dashboards, scheduled reports) and we also spin up M***e for advanced SQL ad-hoc analyses 2. d***t Environment Management • We use d***t C***d with a Git-branch workflow: dev, staging and prod environments each map to a branch • Every PR triggers an ephemeral schema in S***e for validation (tests, freshness checks, docs) • CI/CD pipelines run d***t tests and auto,generate documentation before merge 3. Experiment Orchestration & Feature Flagging • Orchestration: A***w pipelines kick off tests, log exposure events and feed results back into S***e for analysis • Statistical checks and post-test reporting are handled via our Python/R libraries • Feature flags: L***y controls rollout, rollback and percentage-based releases across web and mobile Hope this gives you a clear picture,happy to dive into any of these points live on Wednesday’s call. Let me know if there’s anything else you’d like ahead of time! Best, J***e HR Recruiter, StrategyBrain

Hi W***m, Great questions,here’s a deeper look at how we handle testing and ingestion in our pipeline: 1. d***t Testing Strategy • Built-in tests (schema tests for nulls/uniqueness, freshness tests on source tables) run on every PR and nightly in staging. • Custom macros live in our analytics_utils package,for example, we assert expected growth rates in key funnel tables, validate cohort sizes, and enforce business-specific invariants. These macros can be parameterized per model and integrated into our standard test blocks. 2. Monitoring Long-Running Incrementals • Each incremental model emits metadata (row counts, execution time) into a S***e audit table via a lightweight post-run macro. • An A***w sensor polls these metrics; if a run exceeds its SLA (e.g. 30% over baseline), we trigger a S***k alert and auto-retry logic. We also review historical durations in our internal dashboards to spot regressions. 3. A***w & L***y Integration • We use a custom A***w operator (built on the L***y REST API) to pull flag exposure events and land them in S***e. • That operator leverages our standard S***eHook for staging and bulk loads. For lower-volume flags we also support L***y’s native webhook to our ingestion endpoint and route through the same transform pipeline. Happy to unpack any of this further on our call Wednesday. Let me know if there’s another angle you’d like to cover beforehand! Best, J***e