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 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 limitations in a shared knowledge base to enable self-service analytics.
• Contribute to internal engineering guilds, knowledge-sharing sessions, and post-incident reviews for pipeline failures.

Requirements:

• Strong SQL skills with expertise in complex queries, performance optimization, and cost-efficient design on cloud data warehouses ( BigQuery, Redshift ).
• Strong Python scripting for data ingestion, transformation, and validation, with hands-on experience in Pandas, SQLAlchemy, APIs, and automation.

- ETL/ELT- End-to-end ownership of data pipelines, including ingestion, transformation, and loading, with understanding of incremental loads, and backfills.

- Data Modelling- Ability to design dimensional and transactional data models (star/snowflake, SCDs) and translate business needs into optimized table structures.

Good to Have:

• Airflow, dbt, Docker, CI/CD

- GCP/AWS , data warehousing concepts

• BI tools / Streamlit / visualization

Qualifications:

• Bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related quantitative field (or equivalent practical experience).
• 1–3 years of professional experience in data engineering, analytics engineering, or a backend role with significant data pipeline work.
• Strong understanding of data warehouse architecture — know when to use wide denormalised tables vs. normalised models, and the trade-offs of each.
• Familiarity with version control workflows (Git branching strategies, pull requests, code reviews) and agile development practices.
• A data quality mindset — you instinctively validate assumptions, add assertions to pipelines, and treat silent data failures as critical incidents.

Life at WebEngage :

• We take transparency very seriously. Along with a full view of team goals, get a top-level view across the board with our monthly & quarterly town hall meetings.
• A highly inclusive work culture that promotes a relaxed, creative and productive environment.
• Practice autonomy, open communication, growth opportunities,while maintaining a perfect work-life balance

Perks & Benefits:

• Learning is a way of life. Unlock your full potential backed with cutting-edge tools and mentorship (Macbook for Engagers!)
• Get the best in class medical insurance (with Covid Care facilities), programs for taking care of your mental health, and a Contemporary Leave Policy (beyond sick leaves)

Explore more here:

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Do you think you fit the bill? Come along, letʼs redefine the future of Marketing Automation!

Originally posted on Himalayas