MadTech Architect

In this role, you will serve as both the technical architect and a hands-on implementation lead. You will drive discussions with core engineering teams, collaborate with key external partners (Google, GrowthLoop, and MetaRouter), and build real-time agentic AI workflows using Google Reasoning Engine, GCP Cloud Spanner, GCP BigQuery, and modern martech orchestration engines. Responsibilities

• Lead technical discussions, design reviews, and roadmap alignment with core engineering implementation groups, enterprise stakeholders, and technology partners (Google, GrowthLoop, MetaRouter).
• Hands-on Solutioning & Architecture[strong data-path-to-node=”9,1,0” data-index-in-node=”0”]: Architect and co-implement real-time and batch pipelines across Google Cloud Platform (GCP) and Google Marketing Platform (GMP).
• Agentic AI & Decisioning Engine Deployment: Lead the integration of Google Reasoning Engine for dynamic offer prioritization, Next Best Offer (NBO), and personalized campaign content generation.
• Data Integration & Ingestion: Oversee client-side real-time tag routing via MetaRouter, real-time event streaming via GCP Pub/Sub, and low-latency profile storage via GCP Cloud Spanner.
• Audience & Journey Orchestration: Work closely with GrowthLoop for audience membership/segmentation and GrowthLoop Orchestration (Journey Engine) to execute business rules and instant triggers.
• Cross-Functional Collaboration: Partner with cross functional technology teams across different channels like POS, Digital & Assisted.

• Experience: 7+ years in MarTech/AdTech architecture, Cloud Data Engineering, or AI Solution Architecture.
• GCP Expertise: Deep experience across Google Cloud ecosystem, including Cloud Spanner, BigQuery, Pub/Sub, and Vertex AI / Google Reasoning Engine.
• Desired Technologies: Hands-on experience or familiarity with MetaRouter, GrowthLoop, Optimizely, and Adobe ecosystem tooling (AEM/AEP etc).
• AI & Agentic Workflows: Proven experience designing real-time recommendation/decision engines using LLMs, Agentic AI, or ML models.
• Data Engineering & Streaming: Deep knowledge of real-time streaming (Pub/Sub, websockets, low-latency key-value/document stores) and batch ETL/ELT pipelines.
• Leadership: Exceptional communication skills with a proven track record of bridging the gap between high-level business requirements and hands-on technical execution.