Padmi
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PadSplit

coliving marketplace · affordable housing

Marketing Technology Analyst (MarTech)

Remote · BR$55k–$64k/yrPosted 2 months ago
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Own Segment CDP governance: Maintain and evolve the event taxonomy across web, mobile, and server-side sources. Own cross domain and cross identity mapping. Audit data quality, resolve identity resolution issues, and ensure every marketing tool downstream receives clean, reliable data.

Own Rockerbox attribution: Serve as the day-to-day owner of our multi-touch attribution platform. Diagnose channel-level performance, investigate attribution anomalies (including consent tool impacts), and build the feedback loops that inform paid media spend decisions.

Build and maintain marketing measurement infrastructure: Develop and document the measurement layer for all Member and Host Growth experiments — owning success metrics integrity, building pre/post cohorts, and ensuring statistical validity before results are acted upon acting as a consultative partner to marketing.

Support lifecycle automation analytics: Partner with our Marketing Automation team (Iterable) to ensure behavioral triggers, segmentation logic, and campaign performance data are accurate and actionable. Audit automation flows for data integrity.

Build AI-augmented marketing analytics workflows: Identify repetitive analysis tasks — weekly reporting, anomaly detection, data quality checks — and replace them with AI-powered pipelines. You will be expected to actively prototype and ship AI workflows, not just evaluate them.

Partner across data, product, and paid media: Collaborate with our Data team (Snowflake warehouse), Performance Marketing (Rockerbox, AppsFlyer, Google/Meta), and Product (Segment instrumentation on new features) to ensure the measurement layer scales as the business does.

Build and maintain marketing dashboards: Own the marketing measurement layer in Hex and other BI tools. Create dashboards that give the team a single source of truth on CAC, conversion velocity, funnel efficiency, and channel-level attribution.

Diagnose attribution and data quality issues: When a metric looks wrong, you investigate and resolve it — not escalate it. You own the answer.

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