AI Data Architect

AI Data ArchitectLocation: Menlo Park CARole Summary: Senior Data Architect/Engineer with 10+ years building large-scale AdTech platforms spanning ad serving, targeting, attribution, bidding, measurement, and real-time analytics. Requires strong Data Streaming, Python, and Spark skills plus proven experience delivering scalable data systems for Data science/ML workloads.Key Responsibilities: Lead architecture for batch and real-time AdTech data platforms supporting delivery, Ad targeting, audience intelligence, and analytics. Design scalable data models and distributed systems for personalization, bidding, attribution, fraud detection, and measurement. Drive engineering decisions across ingestion, ETL/ELT, streaming, storage, and Data Science models using Spark, Kafka and Python. Partner cross-functionally to deliver reliable, privacy-aware, cost-efficient platforms while mentoring teams and guiding technical direction.Required Skills: BS/MS in Computer Science, Engineering, Data Science, or related field. 10+ years in software/data/platform engineering with strong AdTech expertise across ad serving, targeting, bidding, attribution, and measurement. Expertise in generating insights, experimentation and optimization to characterize performance. Expert in Streaming data, Python and Spark; proven success building large-scale distributed data platforms and production-grade data pipelines. Good understanding of enterprise system architecture. Hands-on with Spark, Kafka, HBase, Hive, Presto, Flink, Airflow/Beam, SQL/NoSQL, cloud platforms, and AI/ML data enablement.Preferred Qualifications: Experience in digital advertising, retail media, audience platforms, or marketing measurement. Ability to interpret performance metrics, conduct A/B testing, and use analytics tools like Google Analytics 4 (GA4) to track user behavior and Return on Ad Spend (ROAS). Understanding of Google Ads Scripts or rule-based automation to adjust bids and pause campaigns automatically based on real-time triggers. Exposure to recommendation systems, experimentation, A/B testing, or real-time decisioning. Knowledge of data privacy frameworks, ad-tech regulations, Kubernetes, Docker, and microservices. Success Traits: Ownership, architectural judgment, hands-on execution, cross-functional influence, and an ability to simplify complex AdTech data problems.