Act as the bridge between technology and execution, managing the end-to-end lifecycle of programmatic deals for OpenX Partners.
Lead the end-to-end setup, QA, and deployment of deals, ensuring all inventory and data segments are aligned with client objectives.
Build premium audience packages using contextual data triggers and sitelist curation across Display, OLV, and CTV formats.
Proactively monitor deal health, pacing, and delivery. Identify “under-the-hood” yield opportunities to scale successful campaigns and optimize toward client-defined KPI goals such as CTR, CPA, VCR and Viewability.
Act as the primary point of contact for technical troubleshooting, resolving issues related to scale, bidding, and overall deal health.
Skilled at analyzing large data sets to uncover trends, diagnose issues, and drive solutions.
Help define and document "best-in-class" operational workflows to improve team efficiency and platform scalability.
Partner with commercial teams to advise clients on the most effective transactional setups within the OpenX platform.
Work closely with Product and Sales Engineering to pilot new features and provide feedback that drives the OpenX product roadmap.
Requirements
3–5 years of professional experience, with at least 2+ years of hands-on DSP/SSP, Tech Partner or Publisher experience (programmatic background is essential).
Deep understanding of AdTech protocols (RTB, OpenRTB) and the nuances of different formats (CTV, Mobile, Desktop).
Advanced Excel skills (Pivot Tables, VLOOKUPs, data modeling) and experience with visualization tools like BigQuery, LookerStudio Sigview.
Proven ability to manage multiple parallel projects in a fast-paced environment. Experience with Salesforce is a plus.
A solid grasp of data privacy regulations (GDPR, CCPA) and their impact on programmatic targeting.
A "self-starter" mentality with the ability to communicate complex technical concepts to non-technical stakeholders.
Able to manage tasks autonomously, prioritize effectively, and deliver high-quality work with minimal direct.