At Gopuff , data sits at the heart of our strategy. We are reimagining how people purchase everyday essentials, from snacks to household goods to alcohol, all delivered in minutes. To fuel this mission, we are seeking a Data Engineer to design, build, and scale the data systems that power insights, experimentation, and operational excellence across the company.
As a Data Engineer, you will play a critical role in shaping Gopuff ’s modern data platform. You’ll architect reliable pipelines, create insights ready data products, and collaborate closely with analytics, product, and engineering teams to ensure data is trustworthy, discoverable, and ready for action. This is a highly technical, hands-on role for an engineer who wants to solve complex data problems at scale and directly influence business outcomes.
Responsibilities
- Design, build, and maintain scalable batch and real-time data pipelines that power analytics, experimentation, and machine learning
- Contribute to the architecture and maintenance of the Data Platform, ensuring systems are performant, cost-efficient and scalable
- Partner cross-functionally with analytics, product, engineering and operations to deliver high-quality data solutions that drive measurable business impact
- Develop and maintain curated, well-modeled datasets that serve as trusted sources of truth across the organization
- Champion data quality, reliability, and observability by implementing best practices in testing, monitoring, lineage, and incident response
- Contribute to team standards, patterns, and best practices
- Drive improvements to infrastructure, developer workflows, CI/CD, and data platform tooling
Preferred Qualifications
- 3-5 years of experience in data engineering or software engineering with a strong focus on data platform development
- Proven experience building and scaling modern data platforms and delivering high-impact data solutions
- Strong communication skills and the ability to work closely with technical and non-technical partners
- Passion for building reliable, accessible, and high-quality data products
Technical Expertise
- Strong proficiency in Python and SQL
- Experience with modern cloud data warehouses and lakes (e.g., Snowflake, BigQuery, Databricks)
- Experience building batch pipelines using DAG-based orchestrators (e.g., Dagster, Airflow)
- Experience with event-driven architectures using Kafka, Kinesis, or Event Hubs
- Experience developing real-time or streaming pipelines using Apache Beam, Flink, or Spark Streaming
- Experience deploying applications and services to Kubernetes and using tools such as ArgoCD, Helm or Istio
- Experience implementing DevOps concepts within data workflows (CI/CD, observability, monitoring, lineage)
- Experience with Infrastructure-as