Enable event-driven data flows for audience activation into downstream systems
Ensure scalable and reliable stream processing architectures
Data Platform & Performance Optimization:
Optimize audience preview (low-latency queries) and materialization (batch pipelines) Work with: Databricks / Spark for large-scale processing Delta Lake for storage and reliability Click House / Pinot for high-performance analytical queries Use Redis for caching and fast data access Backend & API Development Build scalable APIs and services using Python (FastAPI preferred) Design robust microservices and distributed systems
Ensure high performance, availability, and reliability Cloud & DevOps Deploy and manage services on Kubernetes / AKS Implement CI/CD, monitoring, and scaling strategies
Ensure fault-tolerant and resilient systems
Required Skills:
Strong Python with backend frameworks (FastAPI preferred)
Experience with Spark, Databricks
Strong knowledge of Delta Lake and ClickHouse/Pinot Hands-on with Kafka / Event Hubs and Redis
Strong understanding of distributed systems and streaming architectures
Experience with Kubernetes / AKS
Nice to Have:
Experience with Customer Data Platforms (CDP) / audience systems
Exposure to identity resolution and customer 360 solutions
Experience with large-scale datasets (TB/PB)
Key Expectations:
Strong ownership of backend + data platform components
Ability to design low-latency, high-scale systems
Experience in real-time and batch data processing Strong problem-solving and system optimization skills Business Impact
Enable scalable audience segmentation, customer profile serving, and real-time activation, driving personalization and engagement at scale