Data Engineer

About [Employer]:

[Employer] 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. [Employer] 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, [Employer] 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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[Employer] BLACK: our AI-native layer that brings Agentic capabilities to engagement.

Learn more about us at [link removed]

About The Role:

[Employer]’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.

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

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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

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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 [Employer]:

• 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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• [Employer]?s=09

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

[Employer] aims to be an equal opportunity employer. We strongly believe that when people feel respected and included they can be more creative, innovative, and successful. We believe that change is the only constant and are in the process and will continue to be in process with changing times to adapt and advance diversity and inclusion. We take affirmative action to ensure equal opportunity and complete non-disclosure of all applicants without any regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or any other characteristics not mentioned hereinabove which are protected under the law of the soil.

Originally posted on [Employer]

Timezone: UTC+5.5 to UTC+5.5.