The Krazy Coupon Lady
Senior Director, Advertising Operations & AI Enablement
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Job Description
The Krazy Coupon Lady (KCL) is a female-founder-owned shopping intelligence company on a mission to give smart shoppers the advantage by turning our expertise into their indispensable shopping companion. Millions of shoppers come back to us before every purchase, and we're hiring the people who'll shape what they find and why they keep coming back. We move fast, care deeply about our audience, and believe great work happens when smart people bring clarity, creativity, and accountability to the table.
You may have noticed that our headquarters are in Boise, ID, but our workforce is fully remote in the following states: Arkansas, California, Colorado, Connecticut, Florida, Georgia, Idaho, Illinois, Indiana, Iowa, Minnesota, New Jersey, New York, North Carolina, Ohio, Pennsylvania, South Carolina, South Dakota, Tennessee, Texas, Utah, Virginia, Washington, or Wisconsin. All team-wide meetings are scheduled to be inclusive of U.S. time zones.
KCL is standing up a dedicated Advertising Operations & AI Enablement function, and this role owns it end to end: tag management, ad trafficking, and the integrity of the statistics we put in front of our clients. We need a senior leader to define the strategy and stand up the stack behind it, starting with selecting the tag management platform and the ad server KCL will standardize on. Reporting directly to the Head of Product inside the Data team, this Senior Director will partner with Data Engineering and Analytics on where event data lands, and with Sales on what KCL can credibly commit to in client contracts. Success looks like a best-in-class tag management and ad serving architecture that is governed, scalable, and able to absorb the complexity of KCL's product and marketing requirements. This work sits in an environment spanning Segment, Amplitude, Databricks, and the major ad and pixel ecosystems. We expect this leader to use AI tooling to move faster than a function this size normally could, and to e
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