Data Engineer - I

About GoKwik

GoKwik is a growth operating system designed to power D2C and eCommerce brands—from checkout optimisation and reducing return-to-origin (RTO) to payments, retention, and post-purchase engagement. Today, GoKwik enables 15,000+ merchants globally and processes approximately $2B in annual GMV. Backed by RTP Global, Z47, Peak XV, and Think Investments, we are building AI-powered infrastructure to enable faster, smarter commerce outcomes.

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Why This Role Matters

As a Data Engineer I, you will build and maintain data pipelines that power GoKwik's analytics, machine learning models, and business-critical applications. You will work with modern data engineering technologies, including Apache Iceberg, Trino, Spark, and Kubernetes, while developing a strong understanding of distributed data systems and platform engineering.

This role goes beyond traditional ETL development. You will be expected to understand the systems behind the pipelines, ensure data correctness, optimise query performance and cost, and use AI tools effectively while validating the quality of their output. You will progressively expand your ownership into data platform engineering, orchestration, infrastructure, real-time data serving, and governance.

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What You'll Own

Data Pipeline Development & Management

• Design, build, and maintain end-to-end data pipelines, from source ingestion and schema definition to production-ready silver and gold data models.
• Develop reliable and reusable data transformation workflows using SQL, Python, Trino, and Spark.
• Implement incremental data processing, MERGE operations, and idempotent jobs to ensure consistent and efficient data delivery.
• Onboard new data sources and migrate legacy pipelines to standardised data processing frameworks.
• Work with structured and semi-structured data across multiple source systems.

Data Quality & Reliability

• Implement data quality checks, validation rules, deduplication logic, and safeguards for edge cases.
• Reconcile transformed data against source systems to identify discrepancies and ensure accuracy.
• Build monitoring and alerting mechanisms to detect data issues before they affect downstream applications and reporting.
• Ensure data pipelines are reliable, maintainable, and resilient to failures.
• Document data models, dependencies, and data quality requirements.

Performance & Cost Optimisation

• Analyse and optimise SQL queries and distributed data processing jobs for performance and cost efficiency.
• Understand partitioning, data layout, query execution, and resource utilisation to improve processing efficiency.