Data Engineer, Dandy

• Proven Experience: 4+ years of experience as a Data Engineer, with a strong background in building production-ready data workflows and data pipeline optimization. You have a track record of designing and maintaining scalable, high-performance systems that are reliable and easy to manage in production
• Hands-On Technical Expertise: Proficient in Python, Airflow/Dagster, DBT, and cloud platforms like AWS or GCP. You understand how to build and maintain robust ETL processes and data storage solutions that can scale with the business
• Production-Ready Data Systems: You have hands-on experience in productionizing data workflows, focusing on performance, monitoring, and resilience. You know how to ensure data pipelines run efficiently and at scale while maintaining high data quality
• Collaborative Problem Solver: You excel at working with cross-functional teams, communicating technical concepts to non-technical stakeholders, and aligning team efforts to meet business goals. You’re driven by the challenge of solving complex problems and building effective solutions
• Growth-Oriented: You thrive in a high-growth, evolving environment and are eager to take on new challenges. You’re committed to continuous learning and actively seek opportunities to expand your technical and leadership skills

Desirable

• You’ll work across multiple domains, from product analytics to supply chain optimization, ensuring our data systems are scalable, reliable, and production-ready
• Lead Data Infrastructure Development: Design, build, and maintain scalable data pipelines and ETL processes that power cross-functional teams such as product, operations, and logistics. Help lay the foundation for our data infrastructure as we evolve into a mature, high-performing engineering team
• Contribute to the Data Platform: Play a key role in building and enhancing Dandy's data platform—the foundation upon which the entire organization can scale its data-driven decision-making. You'll ensure that the platform is robust, scalable, and integrates seamlessly with data sources, tools, and stakeholders across the business
• Take Ownership of Data Pipelines: Own the entire lifecycle of data pipelines—from data collection and transformation to storage and delivery. Focus on building production-ready, efficient, and resilient data systems
• Shape Engineering Best Practices: Drive the adoption of best practices for data engineering, documentation, pipeline optimization, and testing. Establish and maintain strong coding standards, data quality processes, and CI/CD practices to ensure data pipelines run smoothly at scale
• Collaborate Across Teams: Work closely with data analysts, product managers, and other internal stakeholders to understand their data needs and deliver accurate, reliable solutions that drive actionable insights and decisions
• Optimize and Maintain Workflows: Beyond building basic pipelines, you will focus on productionizing data workflows, ensuring they are optimized for reliability, efficiency, and scalability. Develop monitoring and alerting systems to proactively address potential issues
• Documentation & Knowledge Sharing: Create and maintain clear, detailed documentation for data architecture, pipeline workflows, and team processes. Help foster a culture of knowledge-sharing and self-sufficiency across the team