Senior Performance Manager, Opendoor

• 7+ years operating and scaling paid search across Google Ads and Microsoft Ads with multi-market complexity
• Deep expertise with modern search (broad match + Smart Bidding), audience strategies, and RSA/asset testing at scale
• Hands-on AI/automation experience: platform AI features, scripts/API, LLM-assisted workflows for ad ops and analysis; comfort evaluating and integrating new AI tools responsibly
• Experimentation and measurement chops: geo experiments, incrementality testing, pre/post analysis, value-based bidding; MMM/MTA familiarity a plus
• Data and tooling fluency: GTM, GA4 (or equivalent), BI dashboards; familiarity with cloud data warehouses (e.g., Snowflake) and data QA best practices; SQL is highly preferred
• Proven success migrating to higher-intent optimization (enhanced conversions, offline conversion imports, consent-aware tagging) and tying media to downstream business impact
• Exceptional communication and stakeholder management; can translate complexity into clear decisions and influence across Marketing, Product, Analytics, and Finance
• Bias to action, high ownership, and meticulous operational hygiene (naming, labels, budgets, and pacing)
• Excellent communication, prioritization, and stakeholder management; thrives in ambiguity, moves with urgency, and maintains a high quality bar
• Ownership mindset with flexibility to switch between strategy and hands-on execution

Desirable

• We’re hiring a Senior Paid Search Manager to lead strategy, execution, experimentation, and measurement across Google Ads and Microsoft Ads in a dynamic, multi-market footprint
• You’ll push the frontier on AI-driven search, using modern search best practices—while building a rigorous testing framework and connecting paid search with SEO, AEO, and Social Search to win the evolving SERP
• You’ll partner closely with Analytics, Product, Data Science, and Finance to align on the right optimization signals (e.g. ToF and qualified downstream events), ensure clean data pipelines, and drive measurable, durable growth
• Own account structure, campaign taxonomy, bidding strategy, and budget/pacing strategy
• Architect Performance Max (asset groups, audience signals, feeds, creative inputs) and core Search (RSA; DSA where appropriate)
• Leverage platform AI and custom automation (scripts/API) for query mapping, asset/creative generation and rotation, anomaly detection, bid/budget reallocation, and pacing
• Pilot and productionize LLM-powered workflows for keyword/negative expansion, ad copy/asset ideation, and performance summarization
• Design and run structured experiments (geo splits, incrementality tests, asset/audience match type tests) with pre-registered hypotheses, power checks, and clear decision rules
• Maintain an experiment registry and codify learnings; scale winners quickly and sunset underperformers
• Partner with Analytics/Eng to implement enhanced conversions, offline conversion imports, and robust tagging; maintain auditable event taxonomies and conversion labels
• Shift optimization toward higher-intent primary actions, balancing low- vs. high-latency events; implement value rules and tCPA/tROAS aligned to drive efficient CAC
• Ensure reliable data flows into warehouse/BI (e.g., Snowflake + QuickSight) and maintain single source-of-truth dashboards
• Manage DMA/City/ZIP targeting, on/off market lists, exclusions, and budget allocation by region; ensure brand defense and healthy non-brand coverage
• Build a unified query universe and avoid cannibalization; coordinate with SEO on content pillars, schema/FAQs, and internal linking to improve organic rankings and AI Overviews presence
• Partner with Brand/Social to translate search insights into search-first content (YouTube, Reddit, TikTok), connect creator/social search to paid capture, and harmonize SERP/share-of-answer strategy
• Collaborate with Product/Eng on site speed, landing experiences, and signal latency reduction; align with Finance/Procurement on budgets, invoicing, and reconciliations
• Manage Google/Microsoft relationships; prioritize relevant betas and escalate technical issues (API, data discrepancies) to resolution
• Uphold privacy, compliance, and category policies (e.g., housing-related ad policies); enforce QA and brand safety standards
• Maintain clear SOPs, naming conventions, SLAs, and QA checklists to ensure reliable operations as the program grows
• How We Measure Success
• Efficiency and impact: CAC/CPA, CPQL, incremental contribution, qualified pipeline
• Brand vs. non-brand efficiency; impression share/share of voice
• Experiment velocity and impact: test cadence, time-to-decision, percent of budget behind proven winners, and documented learnings adopted cross-channel
• Data quality and automation: tag/conversion health SLAs, offline match rates, dashboard accuracy, percent of workflows automated, anomaly detection
• Convergence impact: blended paid+organic SOV on priority queries, reduction in cannibalization, presence/share in AI Overviews and Social Search, and improvements to search-driven landing experiences