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.comAbout 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 patterns.

• Enforce partitioning, clustering, and materialised view strategies to keep warehouse costs under control while maintaining sub-second query performance.

Code Quality & Engineering Best Practices

• Write clean, modular, well-tested Python and SQL code. Follow DRY principles, use version control (Git), and participate in peer code reviews.

• Build reusable transformation frameworks using dbt or equivalent tooling, with proper documentation and testing at every layer (staging → intermediate → mart).

• Containerise data services with Docker and automate deployments via CI/CD pipelines (GitHub

Actions / GitLab CI).BI, Visualization & Analytics

• Build interactive dashboards and analytical tools using Streamlit, enabling stakeholders to explore metrics, run ad-hoc analyses, and make data-driven decisions without engineering dependency.

• Design and maintain BI layers — semantic models, KPI definitions, and pre-aggregated mart tables that serve as the single source of truth for reporting across teams.

• Translate raw data into compelling visual narratives using libraries like Plotly, Matplotlib, or Altair; present findings to both technical and non-technical audiences.

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