Build and operate enterprise-scale data pipelines and lakehouse platforms on GCP. Support audience analytics, content intelligence, customer 360, advertising analytics and AI-ready data products.

Key Outcomes · Deliver production-grade data pipelines with strong SLAs and observability. · Implement scalable batch and streaming architectures. · Improve data quality, governance and lineage. · Enable self-service analytics through curated BigQuery data products. Responsibilities · Design and develop GCP-native data pipelines. · Build ingestion frameworks for structured, semi-structured and streaming data. · Implement ELT/ETL workloads using Dataflow, Dataproc, Composer and dbt. · Develop reusable data models in BigQuery. · Support customer, content and advertising analytics workloads. · Collaborate with Data Architects, AI Engineers and DevOps teams. · Implement automated testing, monitoring and CI/CD. GCP Technical Skills · BigQuery · Dataflow · Dataproc · Cloud Composer · Pub/Sub · Cloud Storage · BigLake · Dataplex · Looker · Vertex AI data integration Data Engineering Skills · Python · SQL · PySpark · dbt · Apache Beam · Data Modeling · CDC Ingestion · Data Warehousing · Lakehouse Architecture Streaming & Real-Time Processing · Pub/Sub · Kafka · Event-driven architectures · Real-time audience analytics · Streaming ETL and transformations Digital Media Experience Preferred · Audience Measurement and Engagement Analytics · Advertising Performance Analytics · Programmatic Advertising Data · Content Consumption Analytics · Customer Segmentation and Personalization · Subscriber and Retention Analytics

Experience: 5-8 years · Location: Bangalore, India · Cloud Platform: Google Cloud Platform (GCP) · Domain Exposure: Digital Media, AdTech, MarTech, Audience Analytics preferred · Hands-on engineering role with strong Python and SQL expertise