Senior Full Stack Engineer (Audience Builder & CDP)

Hello Everyone, Hope you are doing good!!!! My name is Pavan and I work with SPAR Information System., I have a great opportunity for you, please find the job details below, if you are interested in applying please send me your updated resume and best time for you to discuss about this opportunity in details. Role: Senior Full Stack Engineer (Audience Builder & CDP) Location: Remote Duration: Long term contract Based on the JD, the strongest candidates are those who combine: React/TypeScript Databricks/Delta Lake Kafka/Event Hubs Audience Builder/CDP experience AI-assisted segmentation exposure Biggest Hiring Risks Candidates may have: React . Node.js.APIs But lack: Audience segmentation .Databricks,Delta Lake,Kafka,CDP knowledge These candidates may look technically strong but require significant ramp-up A great candidate typically comes from one of these backgrounds Option 1 CDP / MarTech Examples: Adobe Experience Platform Real-Time CDP Audience Manager Salesforce Data Cloud Option 2 Customer Analytics Platforms Examples: Segment Customer 360 Marketing platforms Option 3 Full Stack + Data Engineering Experience with: React Node/Python Databricks Kafka Customer data products Critical Skills (Must Have) 1. React + TypeScript + Next.js Why? Because the Audience Builder UI itself is complex. Examples: Rule builders Drag-and-drop conditions Nested AND/OR groups Audience preview screens Customer profile views Candidates should have built complex business applications, not just websites. 2. API Design The engineer will create APIs for: Audience creation Audience updates Audience preview Audience activation Look for: Node.js Python REST API Microservices 3. Databricks / Delta Lake This is one of the biggest differentiators. Many React engineers won't have this experience. Discussion point: Audience calculations may run against hundreds of millions of customer records. Databricks is likely the primary audience computation platform. Good candidates should understand: Spark Delta Lake Partitioning Query optimization Large-scale datasets 4. ClickHouse Used for: Fast audience preview Real-time analytics Interactive querying Meeting talking point: Databricks computes the audience, ClickHouse serves low-latency previews and analytics. 5. Kafka / Event Hubs Role requires event-driven thinking. Examples: Customer actions generate events: Plain Text Purchase Login Page View Cart Abandonment These events update audience membership in near real time. Good candidates should understand: Event streaming Consumer groups Message ordering Retry patterns 6. Redis Likely used for: Audience preview caching Session storage Temporary counts Not usually a primary screening area but useful. AI Component This separates the role from a traditional CDP role. The JD specifically says: Build AI-assisted segmentation and customer preview experiences Examples: User types: Thanks & Regards, Pavan Raikhelkar LEAD TALENT ACQUISITION SPECIALIST Direct Number:- Fax : Email: Website: (An E-verify Company) NOTE: We respect your online privacy. This is not an unsolicited mail. Under bill 1618 title III passed by the 105th us congress this mail cannot be considered Spam as long as we include contact information and a method to be removed from our mailing list. If you are not interested in receiving our e-mails, please reply with a "REMOVE" in the subject line. We apologize for any inconvenience caused by this mail.