The Role
Build and maintain reliable data pipelines using SQL, Python, Trino, and Spark. Responsibilities include data ingestion, transformation, quality validation, monitoring, performance and cost optimization, production troubleshooting, and documentation. The role also contributes to data platform engineering involving Kubernetes, GitOps, cloud infrastructure, orchestration, real-time data serving, governance, testing, and code reviews. Candidates should understand distributed processing, lakehouse and warehouse fundamentals, and use AI-assisted development tools responsibly.
Summary Generated by Built In
About GoKwik
Why This Role Matters
What You'll OwnData Pipeline Development & Management
Who You Are
Why 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 OwnData 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.
- 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.
- 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.
- 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.
- 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.
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.
Skills Required
- 1-3 years of experience building data pipelines or data platform systems
- Strong SQL skills, including query debugging and data discrepancy investigation
- Solid Python programming skills
- Hands-on experience with at least one distributed data processing or query engine such as Spark, Trino, or Hive
- Understanding of data warehousing and lakehouse fundamentals, including facts, dimensions, partitioning, incremental loads, and data modeling
- Strong debugging and root-cause analysis skills
- Experience writing tests, participating in code reviews, and following software engineering best practices
- Ability to communicate technical issues clearly and collaborate cross-functionally
- Ability to use AI development tools while validating output for correctness, performance, and reliability
- PySpark experience
- Strong internships or relevant projects for entry-level candidates
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The Company
What We Do
GoKwik is a data & technology led enabler, building a full-stack solution suite for eCommerce and D2C brands to help them unlock business growth. Embarked on a mission to democratise the shopping experience, GoKwik enables eCommerce brands to deliver superlative customer experience across the shopping funnel thereby boosting conversion rates and revenue growth. It also solves for other critical pain points of the industry such as COD RTO (Return to Origin) and helps brands manage the RTO problem while offering COD as a payment channel. With its recent addition of a third product: KwikChat, GoKwik is solving for low ROIs on marketing campaigns through 30+ Whatsapp use cases such as abandoned cart recovery, click to whatsapp ad campaigns & headless checkout. 1 in 3 shoppers is already shopping on the GoKwik network that has helped 500+ brands scale their businesses with higher GMV realisation & profit margins. It is helmed by Chirag Taneja (Co-Founder and Chief Executive Officer), Vivek Bajpai (Co-Founder and Chief Technology Officer), and Ankush Talwar (Co-Founder and Chief Data Scientist). GoKwik is backed by investors such as Sequoia Capital, Matrix Partners India, RTP Global & Think Investments. GoKwik's team has deep knowledge in the space of eCommerce with people having previous experience in Flipkart, Razorpay, Swiggy, Myntra, Nykaa, and more.
