Full job description
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. Own technical architecture and scale data pipelines for attribution data. Develop real-time bidding algorithms, pacing controls, and predictive models. Build scalable streaming and batch data pipelines for multi-touch attribution datasets. Implement solutions within privacy frameworks like SKAdNetwork and Privacy Sandbox. Innovate identity framework integrations for Desktop post-third-party cookies. Audit data ingestion for flaws affecting attribution accuracy. Write high-performance, low-latency code in Python, Go, Java, or C++. Requirements: 5+ years in performance advertising or programmatic bidding, expertise in attribution models, experience with large-scale data pipelines using Apache Spark, Flink, Kafka, Snowflake, BigQuery, knowledge of OpenRTB, DSP/SSP mechanics, and 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, broadband reimbursement, and office amenities.
What you'll do
- Develop, benchmark, and deploy real-time bidding (RTB) algorithms, pacing controls, and predictive models to maximize ROAS and conversion tracking across CTV, Mobile App, Video, and Desktop
- Design, evaluate, and implement scalable, low-latency streaming and batch data pipelines for complex conversion and multi-touch attribution datasets
- Advance performance capabilities across programmatic formats in an omnichannel environment
- Build robust mechanisms within SKAdNetwork, Attribution API, and Android Privacy Sandbox constraints for Mobile
- Implement cross-device graph integrations, household-level frequency capping, and server-to-server attribution systems for CTV and 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
- Write high-performance, low-latency, and memory-efficient code matching ultra-scaled backend processing trillions of monthly events
Requirements
- 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
- Strong system-level performance benchmarking skills (profiling memory allocation, reducing I/O bottlenecks)
- Bachelor’s degree in engineering or an equivalent degree from a well-known institute/university
Tech stack
PythonGoJavaC++Apache SparkApache FlinkKafkaSnowflakeBigQuerySKAdNetworkPrivacy SandboxOpenRTBUID2LiveRamp ATSMachine Learning
Benefits
Paternity/maternity leaveHealthcare insuranceBroadband reimbursementKitchen with healthy snacks and drinksCatered lunches