Data Engineer, 7shifts

• Experience in data engineering or software engineering
• A track record of building and maintaining production ETL pipelines from source systems to a queryable data warehouse
• Strong programming skills, including proficiency in Python and the ability to write clean, maintainable SQL
• Experience building well-documented data models and working with version control tools such as Git
• The ability to communicate clearly with cross-functional partners, including analysts, product managers, and other stakeholders
• Experience working with cloud-based data platforms
• A curious mindset and a desire to continuously learn and improve data systems
• We know you might not check every box, and that’s okay. If you’re excited about this role, inspired to contribute to a meaningful product, and eager to share ideas that drive real change, we’d love to hear from you

Desirable

• As the Data Engineer at 7shifts, you will own and evolve the pipelines, models, and infrastructure that power data-driven decisions across the business
• You’ll join a small, high-impact Data Engineering team and report to the Manager, Data Engineering, partnering closely with analysts to turn raw data into reliable datasets that support product development, business intelligence, and operational workflows
• Design, build, and maintain ETL pipelines that extract data from sources like Salesforce, Stripe, Zendesk, Google Analytics, and the 7shifts core application into Snowflake, ensuring reliable and observable data ingestion
• Develop and maintain Airflow DAGs that orchestrate the full warehouse sync cycle, including extract, load, transform, and reverse ETL, to ensure timely and predictable data delivery
• Write and review data transformation models, establish consistent patterns, and maintain clear documentation to support reliable, well-structured datasets
• Partner with analytics engineers and data analysts to translate business questions into well-modeled datasets that power dashboards, experimentation, and downstream systems
• Monitor and maintain data quality by identifying and responding to schema changes, stale tables, and pipeline failures
• Contribute to improvements across the data platform, including CI/CD pipelines, Kubernetes-deployed services, and internal tooling that improve reliability and developer experience