The Impact You Will Make

This role gives Risepoint a clearer, faster, and more reliable read on paid media performance and media investment decisions. By owning paid media analytics, agency/vendor reporting QA, experimentation support, MMM/media mix inputs, and media investment diagnostics, this person helps leaders understand where spend is working, where funnel health is breaking down, and where budget should shift to improve lead quality, application progression, and enrollment outcomes.

How You Will Bring Our Mission to Life

The Senior Manager, Channel Media Analytics is the hands-on analytical lead and performance thought partner for paid media within Channel Analytics. This role builds and maintains the reporting, models, diagnostics, recurring routines, and executive-ready analyses that support paid media decisions across partners, programs, verticals, campaigns, and funnel stages. They are expected to be deep in the data, comfortable challenging agency/vendor outputs, able to partner with Data Science and media vendors, and proactive in translating messy performance signals into clear recommendations for media allocation, spend flighting, and channel optimization.

What You Will Do

• Own analytical execution for paid media performance, including spend efficiency, CPL, CPA, funnel conversion rates, campaign pacing, lead quality, application progression, acceptance, and enrollment outcomes.

• Build and maintain Power BI dashboards and SQL-based reporting assets that connect media spend, platform performance, internal funnel data, MMM/media mix outputs, and enrollment results.

• Conduct deep-dive paid media diagnostics across Google Ads, Microsoft Ads, Meta, DV360, branded/nonbranded search, PMax, campaign structure, keywords/search terms, CPC movement, impression share, and competitive/auction dynamics.

• Validate and reconcile agency/vendor reporting against media platforms and internal source-of-truth data; identify definition gaps, tracking issues, and unsupported conclusions.

• Support experimentation, incrementality measurement, and MMM/media mix evolution through pre/post analyses, holdout comparisons, test/control reads, lift measurement, scenario inputs, and clear documentation of assumptions and limitations.

• Build analytical inputs for media investment planning, budget allocation, flighting, and channel mix recommendations in partnership with paid media and marketing leaders.

• Create recurring routines and frameworks that proactively surface paid media performance opportunities, risks, investment tradeoffs, and recommended actions, rather than operating only as a request taker.
• Partner with Data Science, Data Engineering, Analytics Solution, paid media stakeholders, agency teams, vendors, and media platforms to improve measurement products, data reliability, metric definitions, tracking consistency, and reporting automation.
• Translate complex or conflicting paid media performance signals into concise insights, risks, and recommendations for the Sr. Director and senior stakeholders.

What Success Looks Like

• Full command of the paid media data environment: where the data lives, how it connects, what is reliable, and what requires remediation.

• Core paid media performance dashboard live, trusted, and actively used for leadership conversations and recurring performance management.

• A proactive paid media performance framework established, including recurring views, discussion routines, storyline development, and clear recommendations that help shape media team decisions.

• At least one paid media deep dive delivered that clearly explains performance movement, root causes, business implications, and recommended next steps.
• Agency/vendor reporting QA process established, including known reconciliation points, metric definitions, and escalation paths for data issues.
• Initial experimentation, MMM/media mix, and media investment planning infrastructure scoped and underway, with a clear view of short-term and longer-term needs.

How Impact Will be Measured

• Reliability and adoption of paid media dashboards and recurring reporting by Channel Analytics, paid media stakeholders, and leadership.

• Improved speed and quality of paid media performance diagnostics, including faster identification of root causes behind CPL, CPA, conversion, and enrollment changes.

• Quality of recommendations and decision support for paid media allocation, budget flighting, channel mix, and performance optimization.

• Quality of agency/vendor QA, including ability to identify data discrepancies, challenge weak conclusions, and reconcile reporting across systems.
• Usefulness of experimentation, lift measurement, MMM/media mix inputs, and investment planning outputs in supporting budget and channel decisions.

What You’ll Bring to the Team

Experience That Matters Most

• 6+ years of experience in channel analytics, digital marketing analytics, performance marketing analytics, or a related data-driven marketing environment.

• 3+ years of hands-on paid media analytics experience, especially paid search, campaign performance, spend efficiency, conversion tracking, and funnel measurement.

• Advanced SQL and Power BI skills, including complex data extraction, transformation, dashboard development, and executive-ready performance visualization.

• Experience validating media platform, agency/vendor, and internal funnel data; ability to identify tracking, taxonomy, attribution, and conversion-definition issues.

• Strong understanding of funnel analytics from impression/click through lead, application, acceptance, and enrollment outcomes.

• Experience supporting attribution, MMM inputs, media mix analysis, scenario planning, or incrementality testing; methodology ownership is not required, but the ability to partner effectively with Data Science and vendors is required.
• Ability to operate as a proactive thinking partner to paid media teams, translating analytics into recommendations, tradeoffs, and action plans.

Experience That’s Great to Have

• Higher education, enrollment marketing, direct response, or high-consideration lead-generation experience.

• GA4 exposure, campaign taxonomy governance, and experience working with Databricks, BigQuery, Snowflake, or similar data platforms.
• Experience with media platform APIs, self-serve reporting products, or automated data quality monitoring.