Data Engineer - I
Posted Updated
Summary
Data Engineer I at GoKwik builds and maintains data pipelines powering analytics, ML models, and merchant-facing products — from ingestion to silver/gold models, with data quality checks and performance/cost tuning. Core stack includes SQL, Python/PySpark, Spark, Trino, Apache Iceberg, and Kubernetes, with exposure to Pinot/ClickHouse and AI-assisted tooling.
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.
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.
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.
- Identify inefficient queries, unnecessary data scans, and bottlenecks across the data pipeline.
- Apply best practices for working with large-scale datasets and distributed processing systems.
Production Support & Problem Solving
- Investigate production issues across data pipelines and downstream data consumers.
- Conduct root-cause analysis to identify underlying problems and implement long-term fixes.
- Collaborate with Engineering, Analytics, and Data Science teams to resolve data-related issues.
- Participate in code reviews, testing, and continuous improvement of engineering practices.
Data Platform Engineering
- Develop an understanding of data orchestration, infrastructure, and deployment workflows.
- Contribute to platform improvements involving Kubernetes, GitOps, and cloud infrastructure.
- Explore real-time data serving and analytical systems using technologies such as Apache Pinot and ClickHouse.
- Support data governance, documentation, and standardisation initiatives.
- Use AI-assisted development tools to improve productivity while independently validating generated code, queries, and logic.
Who You Are
- Have 1–3 years of experience building data pipelines or data platform systems. Strong internships and relevant projects may be considered for candidates at the entry level.
- Have strong SQL skills and the ability to debug queries, investigate discrepancies, and reason about data correctness.
- Possess solid Python programming skills; experience with PySpark is an advantage.
- Have hands-on experience with at least one distributed data processing or query engine, such as Spark, Trino, or Hive.
- Understand data warehousing and lakehouse fundamentals, including facts and dimensions, partitioning, incremental loads, and data modelling.
- Have a strong debugging mindset and the ability to identify root causes rather than relying only on temporary fixes.
- Are comfortable writing tests, participating in code reviews, and following software engineering best practices.
- Can communicate technical issues clearly and collaborate effectively with cross-functional teams.
- Use AI development tools to improve speed and productivity while validating their outputs for correctness, performance, and reliability.
- Demonstrate curiosity, ownership, and a willingness to learn modern data platform technologies.
Why GoKwik
At GoKwik, you'll work on a modern data platform that supports critical business decisions, machine learning models, and merchant-facing products. You'll gain hands-on experience with distributed data systems, cloud infrastructure, and AI-assisted engineering while solving real-world challenges involving data reliability, scalability, and performance.