Data Engineer

Nous Research

1d

About The Position

Join Nous to build and own the data pipelines behind analytics and model training. The role focuses on long-horizon agentic data at scale, turning millions of multi-step, tool-conditioned interactions into reliable datasets the company can trust. Applicants may be in any time zone, provided they have meaningful overlap with US hours.

Requirements

• At least 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 is appropriate.
• Working knowledge of the data stack, including ingestion, storage, transformation, orchestration, cataloging, and serving, and where responsibilities belong in each layer.
• Strong Python skills and 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, including evaluating whether data is good rather than only whether a pipeline ran.
• Extensive use of AI-assisted development tools, with disciplined review of their output.

Nice To Haves

• 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.

Responsibilities

• Build, operate, and continuously improve data pipelines that feed analytics and model training.
• Deliver reliable internal analytics so teams across Nous can make decisions using their data.
• Transform 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.
• Own production pipeline operations, including handling failures, re-runs, and backfills; 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 through streaming and partitioning, and identify when a workload has outgrown a single machine.
• Communicate clearly to engineers and non-engineers about data location, lineage, quality, limitations, and fitness for use.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed