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

• The role you are applying for in the subject

• Resume or CV

• Cover letter

• Portfolio/GitHub showcasing your work