Data Engineer

About WebEngage : WebEngage is an enterprise-grade customer engagement and retention platform that helps global brands across industries such as e-commerce, fintech, travel, edtech, gaming, media, and consumer apps. and turn data into measurable revenue impact. Trusted by 800+ brands globally, we have strong presence in India, UAE, KSA, SEA, Europe and beyond. WebEngage powers intelligent, real-time engagement across the entire customer lifecycle. We are built for scale. We are built for complexity. We are built for outcomes. At our core, WebEngage is a full-stack retention operating system that combines: A powerful Customer Data Platform (CDP) Real-time behavioral segmentation and intelligence Omnichannel journey orchestration AI-driven personalization and recommendations Deep analytics, experimentation, and revenue attribution WebEngage BLACK: our AI-native layer that brings Agentic capabilities to engagement. Learn more about us at www.webengage.com About The Role: WebEngage ’s Data Engineering team is the backbone of our analytics, personalisation, and machine-learning capabilities. As a Data Engineer, you will own the end-to-end lifecycle of data — from ingesting raw event streams and third-party feeds to building well-modelled, highly reliable datasets that power dashboards, predictive models, and customer journey orchestration. This is a high-impact, hands-on role where you will work at the intersection of product, analytics, and backend engineering. You will be expected to think beyond just moving data — designing for data quality, pipeline observability, cost efficiency, and long-term maintainability from day one. Responsibilities: Pipeline Development & Reliability Design, build, and maintain production-grade ETL/ELT pipelines that ingest data from APIs, databases, event streams (Kafka/Pub-Sub), and flat files into the central data warehouse. Implement idempotent, incremental load patterns with built-in retry logic, dead-letter queues, and SLA-based alerting to ensure zero-data-loss pipelines. Own pipeline observability — set up data freshness checks, row-count validations, schema drift detection, and anomaly alerts using tools like Great Expectations or dbt tests. Data Modelling & Warehouse Design Translate business requirements into clean dimensional models (star/snowflake schemas) and maintain a well-documented data catalogue. Design slowly changing dimensions (SCD Type 1/2), bridge tables, and fact tables optimised for analytical query...

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