Seeking a Senior Performance Advertising Engineer to optimize buyer-side and performance marketing systems. Responsibilities include designing algorithms, building data systems, and refining optimization loops to maximize ROAS across Mobile App, Video, CTV, and Desktop. Role involves ownership of technical architecture and scaling data pipelines for attribution data. Requires expertise in real-time bidding algorithms, pacing controls, predictive models, and scalable streaming and batch data pipelines. Must have experience with SKAdNetwork, Privacy Sandbox, cross-device graph integrations, identity frameworks like UID2 and LiveRamp ATS, and system-level performance benchmarking. Requires 5+ years in software engineering focused on performance advertising or programmatic bidding, strong knowledge of attribution frameworks, distributed data processing frameworks (Apache Spark, Flink, Kafka), cloud data warehouses (Snowflake, BigQuery), OpenRTB protocol, DSP/SSP mechanics, and production-grade machine learning for CTR/CVR prediction. Bachelor’s degree in engineering or equivalent required. Hybrid work schedule with 3 days in office and 2 days remote. Benefits include parental leave, healthcare insurance, broadband reimbursement, and office amenities.
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Implement cross-device graph integrations, household-level frequency capping, and server-to-server attribution systems for CTV & Video
Innovate alternative identity framework integrations (e.g., UID2, LiveRamp ATS) for Desktop to sustain attribution fidelity post-third-party cookies
Audit existing data ingestion points for architectural flaws, data loss, or systemic latencies affecting attribution accuracy and campaign performance
5+ years of software engineering experience focusing on performance advertising, programmatic bidding, or large-scale user-conversion optimization loops
Demonstrated track record of evaluating and implementing multi-touch attribution (MTA), last-touch attribution (LTA), or incrementality testing frameworks in a production environment
Hands-on experience engineering pipelines handling terabyte-to-petabyte scale data via distributed frameworks such as Apache Spark, Apache Flink, Kafka, and cloud data warehouses (e.g., Snowflake, BigQuery)
Strong technical familiarity with technical specs supporting Mobile App (IDFA/GAID loss mitigations), CTV (App Transport Security, IFA standards), and Desktop environments
Deep understanding of the OpenRTB protocol, DSP/SSP mechanics, and supply-path optimization (SPO)
Experience deploying production-grade Machine Learning frameworks for CTR/CVR prediction
