AI Data Engineer

🕒 July 29

🇺🇸 United States – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🚰 Data Engineer

👻 Ghost score 16%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Dscout

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2011

☁️ SaaS

🤝 B2B

💰 $70M Series C - Dscout on 2022-03

SaaS • B2B

Dscout is an experience research SaaS platform that helps teams recruit engaged participants, run usability tests, field studies, diary studies, interviews and media-rich surveys, and analyze qualitative and quantitative feedback. It provides a verified participant panel (“Scouts”), study templates and admin tools, and AI-assisted tagging and analysis to speed insight discovery and decision-making for product and UX teams.

📋 Description

• Design, build, and own the data pipelines that move and transform data from our application and third-party sources into relational databases - keeping them reliable and well-modeled as volume and complexity grow • Partner with analytics engineering to build the data models and reporting that give researchers and teams real visibility into how their work is performing • Own data quality as a first-class concern across ingestion, modeling, and reporting. Catch problems before they reach a model, a dashboard, or a user, and fix them • Build and ship production AI systems the data infrastructure and services that ML features run on • Design and own evaluation systems that tell us whether an AI feature is ready to ship and holding up over time: eval harnesses, test datasets, and production monitoring built as software, not one-off analyses • Set the standard for how data work gets done. Write clearly, share context early, and make the people around you faster

🎯 Requirements

• 5+ years of experience in data engineering, with meaningful exposure to ML systems in production • Deep data engineering experience: you've personally built and owned pipelines that move data from application sources into a warehouse at scale, and you know what breaks, when, and why • Strong Python skills and fluency across the data stack (we use Snowflake and Postgres) • Experience with orchestration tools like Airflow, Dagster, or similar • Experience working with cloud computing environments like GCP or AWS. • A track record of working closely with analytics engineers or data analysts to build reliable, well-documented data models • Hands-on experience shipping AI or ML systems that real users depended on in production, and owning what happened after launch • A genuine point of view on evaluation: you treat evals as something you build, not a report you write • A high-agency mindset. You can take an ambiguous problem and drive it to a working outcome without a fully-scoped ticket • Fluency across the data lifecycle, from production-facing features to the internal analytics that drive decision-making • Comfort working across the data lifecycle, from production-facing features to the internal analytics that drive decision-making • Comfort using AI coding tools (Cursor, Claude Code, Copilot, or similar) as a real part of your workflow

🏖️ Benefits

• A strong and competitive compensation package with a built-in bonus and equity program. • An incredible and progressive benefits package (for both you and your dependents) to support work/life balance, including flexible PTO, 15 company holidays, 12 weeks of paid parental leave, 401k match, and much more. • An education stipend to support your growth & development, and a remote work stipend. • A company that is open and transparent with our team. You will know what is happening and why it matters.