Data Engineer

Job Description

Company Description Dagen.ai is a platform purpose-built for agentic data pipelines that serve both human and machine consumers. The company focuses on pipelines that are intent-aware, self-healing, and orchestrated by AI agents that can reason, act, and learn over time. As AI agents, LLMs, and RAG systems increasingly consume data at machine speed, Dagen.ai enables pipelines that can operate reliably without waiting for manual intervention. This approach positions Dagen.ai at the forefront of the next major shift in data engineering, where machines become first-class consumers of data. Team members have the opportunity to help shape how modern data infrastructure is designed, operated, and scaled.
Role Description This is a full-time, on-site Data Engineer role based in Mountain View, CA. The Data Engineer will design, build, and maintain scalable data pipelines that support agentic workflows and high-volume machine consumption. Day to day, this role includes implementing and optimizing ETL processes, modeling data for analytics and AI use cases, and managing data warehousing solutions to ensure reliability, performance, and data quality. The Data Engineer will collaborate closely with software engineers, ML engineers, and product teams to define data requirements, improve data accessibility, and support experiments and new features. The role also involves monitoring production data systems, troubleshooting issues proactively, and contributing to the evolution of tooling and best practices for agentic pipelines.
Qualifications

Strong data engineering skills, including experience designing and implementing robust data pipelines and working with distributed data processing frameworks.
Proficiency in data modeling and data warehousing, with the ability to design schemas that support analytics, AI, and high-throughput applications.
Hands-on experience with Extract Transform Load (ETL) processes, including building, optimizing, and monitoring ETL workflows in production environments.
Applied data analytics skills, including querying large datasets, generating insights, and collaborating with stakeholders to translate data into decisions.
Proficiency in at least one modern programming language commonly used in data engineering (such as Python, Scala, or Java) and strong SQL skills.
Experience with cloud data platforms (e.g., AWS, GCP, or Azure) and modern data stack tools (e.g., Spark, dbt, Kafka, or similar technologies).
Familiarity with ML/AI workloads, LLMs, or real-time data systems is highly beneficial.
Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
Ability to work collaboratively in an on-site, fast-paced environment, with clear communication and a focus on reliability and ownership.