AI ArchitectAI Data Architect

Role: AI Architect/AI Data Architect
Location - Menlo Park CA ( Onsite DAY1 ) 4 days WFO

Role 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 Qualifications

• 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.