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

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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 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.

Collaboration & Communication

- Partner with product managers, analysts, and data scientists to understand data needs and proactively identify gaps in current data coverage.

- Document data lineage, transformation logic, SLAs, and known li