Lead Data Engineer

The role is based in Los Angeles

Since opening our doors in 2007, sweetgreen has been on a mission to build healthier communities, starting with real food. As we build our digital platform and continue to scale, we are looking for a Lead Data Engineer to architect and lead the development of robust data infrastructure and pipelines for our Marketing and Finance departments. The role is crucial in overseeing the design and implementation of scalable data systems with a strong emphasis on reliability, performance, and data quality. You will be responsible for delivering innovative data engineering solutions that empower our teams with accessible and trustworthy data, while also maturing our capabilities based on your vision for the space.

At sweetgreen, a Lead Data Engineer is more than a pipeline builder. This role is not about "tell me what to build, and I'll create the ETL", this role is about guiding the team in architecting and implementing the right data solutions for our customers and company. Lead Data Engineers are trusted partners to business stakeholders and their teammates and play an important role in driving the collection, transformation, and availability of sweetgreen's data. They are responsible for delivering key infrastructure and data pipelines to power decision making throughout the business. They work with our data analysts and business partners to deliver on our roadmap of data products, new ingestion methods and transformations, and surfacing reliable data sets to ensure that our business teams are empowered to take action against data.

Your Responsibilities

• Advanced Data Architecture and Infrastructure Leadership — Design and implement complex data architectures and scalable infrastructure solutions that support strategic business initiatives and enable competitive advantage. Lead the technical vision for data platform evolution.

• Leadership in Pipeline Development and Optimization — Lead the development and optimization of advanced data pipelines and ETL/ELT processes, ensuring they deliver reliable, performant data flows that align with senior management's strategic needs and SLA requirements.

• Data Engineering and Quality Assurance — Oversee data ingestion, transformation, and storage processes, ensuring high standards of data reliability, performance, and security. Implement and maintain data engineering best practices and monitoring to ensure data quality across the organization.

• Technical Leadership and Mentorship — Act as a technical leader and mentor to data engineering team members and cross-functional partners. Guide architectural decisions and recommend data engineering strategies. Facilitate cross-departmental collaboration to ensure alignment of data engineering initiatives with organizational goals.

• Innovation and Technical Excellence — Lead initiatives to improve and innovate data engineering processes, tools, and infrastructure. Drive the adoption of new technologies and methodologies to enhance data capabilities, system performance, and business outcomes.

• Leading future state — Stay updated on industry best practices, emerging technologies, and architectural patterns to drive innovation and maintain a competitive edge in the market. Foster a culture of learning and experimentation within the team.

Your Accomplishments & Qualifications

• 5+ years of experience in data engineering, data platform development, or a related field, preferably in Marketing or Growth Analytics environments

• Technical leadership mindset and ability to architect scalable and maintainable data solutions that serve diverse business needs

• Strong proficiency in Python and SQL for data processing, transformation, and pipeline development

• Extensive experience with DBT for data transformation and modeling, including advanced features and best practices

• Deep knowledge of Snowflake for data warehousing, including optimization, security, and cost management

• Background knowledge of Astronomer (Apache Airflow) for workflow orchestration is a plus

• Ability to design and operationalize efficient data architectures, including modern data stack components, cloud-native solutions, and data mesh principles

• Track record of delivering scalable data engineering solutions that support analytics, machine learning, and operational use cases

• Experience with the AWS cloud platform and infrastructure as code practices

• Passionate about food. Ideally, you know what you eat, and y