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