Gopuff

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 Senior 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) 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.

• Be innovative. Aim to improve consumer experience for sustainable growth • Build and enhance machine learning, statistical and causal models for product search, ranking and recommendation that support various business goals • Work closely with product and engineering team to develop and deploy solutions with cross-functional support • Build models that support real-time events and internal stakeholder decisions across the business • Present work to business and engineering leadership • Identify gaps in existing data, create data product specs, and work with Engineering teams to implement enhanced data solutions • Are committed to automation and productionalized solutions whenever possible

• Own the end-to-end development and operation of deep learning–based ETA prediction systems—building features, training/testing models, running experiments, monitoring and explaining performance in production. • Collaborate with our business, product, and engineering partners across the organization to vision and deliver data science solutions for practical business problems. • Deeply understand our users, our products, and our data to identify opportunities for creating machine learning, optimization and causal inference solutions to deliver a better customer experience and make our business more efficient. • Build data driven, robust and replicable data science models. • Use experimentation and causal inference to measure the impact of our models on user engagement and business metrics.