About this role.
Gopuff is hiring a full-time Data Engineer to build and scale a modern data platform supporting analytics, experimentation, machine learning, and operations. The role focuses on reliable batch and real-time pipelines, curated data products, data quality, observability, and platform performance. Candidates should have 3–5 years of relevant engineering experience, strong Python and SQL skills, and experience with cloud warehouses or lakes, orchestration, streaming, Kubernetes, and Terraform. The engineer will work cross-functionally with analytics, product, engineering, and operations teams while improving developer workflows and CI/CD practices.
Role DNA
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Job Complexity
4/5Pace & Pressure
4/5Autonomy Level
4/5Communication Load
4/5Salary analysis
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Core skills
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Cover letter sample
Dear Hiring Team,
I am excited to apply for the Data Engineer role at Gopuff. My experience building reliable batch and streaming data pipelines with Python, SQL, cloud data platforms, and orchestration tools aligns closely with your need for scalable, insights-ready data products.
I would bring a strong focus on data quality, observability, infrastructure automation, and collaborative delivery with analytics, product, and engineering partners. I am particularly motivated by the opportunity to improve a modern data platform that supports experimentation and measurable operational outcomes at scale.
Thank you for your consideration. I welcome the opportunity to discuss how I can help Gopuff build trustworthy, efficient, and accessible data systems.
Sample interview questions
I would start by defining data contracts, schema ownership, freshness expectations, and quality checks with upstream and downstream stakeholders. I would implement idempotent processing, automated tests, monitoring for volume and freshness anomalies, lineage, and clear alerting and incident runbooks so failures can be detected and resolved quickly.
I would evaluate latency requirements, event volume, ordering and delivery semantics, schema evolution, failure recovery, and operating cost. For example, I would use Kafka or Kinesis for durable event ingestion and select a processing framework such as Flink, Beam, or Spark Streaming based on stateful processing, windowing, deployment, and team-operability needs.
I would model the data around well-defined business entities and metrics, document definitions, and build curated layers that separate raw ingestion from reusable analytical datasets. I would validate the model with analytics and product partners, establish ownership, and use tests and lineage to protect downstream consumers from unintended changes.
I would use CI to run unit, integration, data-quality, and infrastructure validation tests before changes are merged. For deployment, I would version pipeline and infrastructure artifacts, use Terraform for managed resources, and apply GitOps tooling such as ArgoCD with controlled promotion, rollback procedures, and observability after release.
I would first assess business impact, data scope, and whether the issue is ongoing, then communicate a concise status update to affected partners. I would mitigate the immediate problem through rollback, replay, or isolation where appropriate, document the root cause, and prioritize preventive improvements such as stronger validation, alerting, or dependency safeguards.
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-Code (e.g., Terraform)
Compensation
- Gopuff pays employees based on market pricing and pay may vary depending on your location. The salary range below reflects what we’d reasonably expect to pay candidates. A candidate’s starting pay will be determined based on job-related skills, experience, qualifications, interview performance, and market conditions. These ranges may be modified in the future. Exceptions may be made for exceptional individuals. For additional information on this role’s compensation package, please reach out to the designated recruiter for this role.
- This role is eligible for a discretionary annual cash bonus and participation in Gopuff’s equity incentive plan.
- Remote Base Salary Range: $118,000 – $148,000
Benefits Overview
- Medical/Dental/Vision Insurance
- 401(k) Retirement Savings Plan
- HSA or FSA eligibility
- Long and Short-Term Disability Insurance
- Mental Health Benefits
- Fitness Reimbursement Program
- 25% employee discount & FAM Membership
- Flexible PTO
- Group Life Insurance
- EAP through AllOne Health (formerly Carebridge)
Compensation
Additional Information
The only predictable thing about life is that it’s wildly unpredictable. That’s where we come in. When life does what it does best, customers turn to Gopuff to deliver their everyday essentials, and to get through their day & night, work day and weekend. We’re assembling a team of thinkers, dreamers & risk-takers…the kind of people who know the value of peace of mind in an unpredictable world. (And people who love snacks.)
Like what you’re hearing? Welcome to Gopuff.
#LI-GOPUFF
The Gopuff Fam is committed to an inclusive workplace where we do not discriminate on the basis of race, sex, gender, national origin, religion, sexual orientation, gender identity, marital or familial status, age, ancestry, disability, genetic information, or any other characteristic protected by applicable laws. We believe in diversity and encourage any qualified individual to apply. We are an equal employment opportunity employer.
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