{"id":2639727,"url":"https://alion.io/job/nous-research-data-engineer","title":"Data Engineer","company":{"id":35054,"name":"Nous Research","domain":"nousresearch.com","url":"https://alion.io/company/nous-research","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"middle","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_regions","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["US","AR","BR","CA","MX","AG","AW","BS","BB","BZ","BM","BO","VG","BQ","KY","CL","CR","CW","DM","DO","EC","SV","FK","GF","GL","GD","GP","GT","GY","HT","HN","JM","MQ","MS","NI","PA","PY","PE","PR","KN"],"hiring_countries_total":48,"salary":null,"salary_estimate":{"min_usd":87000,"max_usd":237000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":865},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Dagster","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GitHub","optional":false},{"name":"LLM","optional":false},{"name":"Prefect","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-10-15T18:31:03Z","employer_posted_date":"2026-10-15","last_verified_at":"2026-10-16T10:22:05Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"The role\nJoin 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.\nResponsibilities\nBuild, operate, and continuously improve the data pipelines that feed analytics and model training.\n\nDeliver reliable internal analytics so teams across Nous can make decisions on their own data.\n\nWrangle long-horizon agentic data into structured, well-documented datasets ready for analysis and training.\n\nChoose ETL/ELT approaches based on destination capabilities, reproducibility, latency, and cost, and explain the tradeoffs behind each choice.\n\nOwn pipeline operations in production, including failures, re-runs, and backfills, and debug issues across jobs, storage, permissions, and downstream consumers.\n\nDesign and evolve schemas, storage formats, and data models as needs change.\n\nBuild memory-efficient processing that scales, using streaming and partitioning, and recognize when a workload has outgrown a single machine.\n\nCommunicate clearly about data location, lineage, quality, limitations, and fitness for use to engineers and non-engineers alike.\n\nQualifications\n4+ years of data engineering experience building and operating production data pipelines.\n\nStrong fundamentals in SQL, data modeling, and practical schema design and evolution.\n\nDeep understanding of storage formats and database types, and when each one fits.\n\nA working model of the data stack: ingestion, storage, transformation, orchestration, cataloging, and serving, and where responsibilities belong in each layer.\n\nStrong Python, plus solid shell skills for batch and storage operations.\n\nHands-on cloud experience with object storage, batch compute, and identity and access management.\n\nProduction experience with at least one workflow orchestration system such as Airflow, Dagster, Prefect, or an in-house equivalent.\n\nAn instinct for data quality: you ask \"is the data good?\" and not just \"did the pipeline run?\"\n\nExtensive use of AI-assisted development tools, with disciplined review of what those tools produce.\n\nPreferred\nExperience working with agentic or LLM-generated data, including structured, multi-step, tool-conditioned interactions.\n\nExperience building data pipelines for model training.\n\nExperience processing datasets at the scale of millions of interactions or more.\n\nBackground at data infrastructure, observability, or AI data companies.\n\nHow to apply\nEmail  with the following information:\nThe role you are applying for in the subject\n\nResume or CV\n\nCover letter\n\nPortfolio/GitHub showcasing your work","description_format":"text","description_chars":2783,"description_truncated":false,"requirements":{"experience_years_min":4,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"United 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