Data Engineer

THE ROLE Join Nous to build and own the data pipelines behind our analytics and model training. This role centers on long-horizon agentic data at scale, turning millions of multi-step, tool-conditioned interactions into reliable datasets the whole company can trust. Open to any time zone with meaningful overlap with US hours. RESPONSIBILITIES - Build, operate, and continuously improve the data pipelines that feed analytics and model training. - Deliver reliable internal analytics so teams across Nous can make decisions on their own data. - Wrangle long-horizon agentic data into structured, well-documented datasets ready for analysis and training. - Choose ETL/ELT approaches based on destination capabilities, reproducibility, latency, and cost, and explain the tradeoffs behind each choice. - Own pipeline operations in production, including failures, re-runs, and backfills, and debug issues across jobs, storage, permissions, and downstream consumers. - Design and evolve schemas, storage formats, and data models as needs change. - Build memory-efficient processing that scales, using streaming and partitioning, and recognize when a workload has outgrown a single machine. - Communicate clearly about data location, lineage, quality, limitations, and fitness for use to engineers and non-engineers alike. QUALIFICATIONS - 4+ years of data engineering experience building and operating production data pipelines. - Strong fundamentals in SQL, data modeling, and practical schema design and evolution. - Deep understanding of storage formats and database types, and when each one fits. - A working model of the data stack: ingestion, storage, transformation, orchestration, cataloging, and serving, and where responsibilities belong in each layer. - Strong Python, plus solid shell skills for batch and storage operations. - Hands-on cloud experience with object storage, batch compute, and identity and access management. - Production experience with at least one workflow orchestration system such as Airflow, Dagster, Prefect, or an in-house equivalent. - An instinct for data quality: you ask "is the data good?" and not just "did the pipeline run?" - Extensive use of AI-assisted development tools, with disciplined review of what those tools produce. PREFERRED - Experience working with agentic or LLM-generated data, including structured, multi-step, tool-conditioned interactions. - Experience building data pipelines for model training. - Experience processing datasets at the scale of millions of interactions or more. - Background at data infrastructure, observability, or AI data companies. HOW TO APPLY Email recruiting@nousresearch.com with the following information: - The role you are applying for in the subject - Resume or CV - Cover letter - Portfolio/GitHub showcasing your work