Data Engineer
Apply on Nous Research’s site Posted 6h ago · via Ashby
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
This listing comes from Nous Research’s public job board and links back to it. Maiev never applies on your behalf.