AI Data Engineer

Dscout

AI Data Engineer at Dscout, focusing on data pipelines and ML system evaluation. Collaborating with MLE and analytics teams to build data infrastructure and ensure data quality.

Posted 7/21/2026full-timeRemote • 🇺🇸 United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and owning data pipelines, ensuring data quality, and shipping production AI systems. Proficient in collaborating with analytics teams to create reliable data models and reporting mechanisms.

Highest-signal resume keywords

Data EngineeringPython ProgrammingData Pipeline DevelopmentMachine Learning SystemsCloud Computing (GCP, AWS)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data Pipeline DevelopmentData ModelingMachine Learning SystemsPython ProgrammingData Quality AssuranceProduction MonitoringData Lifecycle ManagementEvaluation Systems DesignOrchestration Tools (Airflow, Dagster)AI Coding Tools (Cursor, Claude Code, Copilot)

Soft Skills

High-Agency MindsetProblem-SolvingCollaborationClear Communication

Tools & Technologies

SnowflakePostgresGCPAWSAirflowDagster

Industry Keywords

Data EngineeringMachine LearningData QualityData AnalyticsProduction Systems

Tech Stack

Tools & technologies

AirflowAWSCloudGoogle Cloud PlatformPostgresPython

About the role

Key responsibilities & impact

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

What you’ll need

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 using AI coding tools (Cursor, Claude Code, Copilot, or similar) as a real part of your workflow

Benefits

Comp & perks

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.