Data Architect

Data Architect – AdTech

Location: Menlo Park, CA

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.

Success Traits

Success Traits: Ownership, architectural judgment, hands-on execution, cross-functional influence, and an ability to simplify complex AdTech data problems.

Thanks & Regards,

Tushar Chauhan

(xxxxxxxxxxxxxxxExt. 1079

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