Job description

About Courtyard Courtyard.io is the fastest-growing collectibles startup, ever. We are revolutionizing the world of collectibles trading by enabling instant liquidity and delivering a high-velocity, immersive experience. From trading cards to comics, we're redefining how people discover, collect, and unlock value. Courtyard is not just another marketplace. All assets are securely vaulted and fully insured, giving collectors peace of mind alongside unmatched speed and simplicity. Whether you're investing, discovering, or curating your dream collection, we've built a platform that's trusted, simple, and built for speed. And we're just getting started. We're a remote-first company hiring across all functions to push the boundaries of what's possible in collectibles and digital ownership. Job Summary Courtyard is hiring a Staff Data Engineer to own the data foundation our pricing and ML run on, from raw external market data all the way to models making decisions in production. We price and buy back assets in real time off a fragmented external market. Turning that into something a model can safely consume, and keeping the model healthy once it's live, is the hardest data problem here. You'll be the senior-most data engineering voice in a lean data org and the person who gets data science work from notebook to production. You'll own architecture and standards rather than a ticket queue. What You'll Do Own external market data ingestion end to end — acquisition, normalization, and delivery into the warehouse Own the canonical item model and the entity resolution that maps messy inbound records onto it Build quality and monitoring that catches gaps and degradation before a stakeholder notices Own the warehouse layer (dbt modeling, tests, documentation) and set schema contracts with product engineering so upstream changes break the build, not the metrics Build the feature and training data layer: point-in-time correct, reproducible, and consistent between training and serving Partner with data scientists to take models from prototype to production — batch and real-time scoring, versioning, CI/CD, and safe rollout and rollback Own model observability — input drift, prediction quality, and retraining triggers — so a degrading model gets caught by an alert, not by a bad decision Productionize LLM-assisted pipelines where they earn their keep, with evaluation and cost controls built in Set the engineering bar for the data org and level up DS on production-path work What We're Looking For 7+ years across data engineering, ML engineering, or ML platform work with real production ownership, at senior or staff scope Experience taming third-party data at volume — unstable sources, schema drift, no advance notice Entity resolution in practice, including the judgment to know when a confident match is wrong You've shipped ML to production alongside data scientists: built the feature pipelines, deployment path, and monitoring for models that served live decisions, not just handed off a table Fluency in where ML breaks in the data: leakage, point-in-time joins, train/serve skew, drift Deep SQL and Python; production dbt with tests and incremental models you actually maintained Strong dimensional modeling — SCD Type 2, incremental loads, backfills, late-arriving data Cloud warehouse depth. We run BigQuery and GCP; Comfortable with orchestration, containers, and CI/CD — you can get a service deployed and keep it observable without being an infrastructure specialist Clear communicator with strong ownership instincts — you say what you'll own, what you won't, and by when Bonus: feature store or model registry experience; LLM evaluation and cost management; pricing or valuation systems; a real interest in trading cards and collectibles What You'll Get In Return A dynamic and engaging environment focused on fostering real growth and innovation Opportunities to create amazing products that our customers truly love and value Comprehensive health insurance packages with dependent coverage Competitive salary with ample opportunities for career advancement and development Enjoy the flexibility of a fully remote work environment 401(k) plan with a 4% employer match to help you plan for the future $100 monthly dogfooding stipend to support trying out our products firsthand Access to employee wellness programs designed to support your overall well-being

Requirements

Must-haves

Own external market data ingestion end to end — acquisition, normalization, and delivery into the warehouse
Own the canonical item model and the entity resolution that maps messy inbound records onto it
Build quality and monitoring that catches gaps and degradation before a stakeholder notices
Own the warehouse layer (dbt modeling, tests, documentation) and set schema contracts with product engineering so upstream changes break the build, not the metrics
Build the feature and training data layer: point-in-time correct, reproducible, and consistent between training and serving
Partner with data scientists to take models from prototype to production — batch and real-time scoring, versioning, CI/CD, and safe rollout and rollback
Own model observability — input drift, prediction quality, and retraining triggers — so a degrading model gets caught by an alert, not by a bad decision
Productionize LLM-assisted pipelines where they earn their keep, with evaluation and cost controls built in
Set the engineering bar for the data org and level up DS on production-path work
7+ years across data engineering, ML engineering, or ML platform work with real production ownership, at senior or staff scope
Experience taming third-party data at volume — unstable sources, schema drift, no advance notice
Entity resolution in practice, including the judgment to know when a confident match is wrong
You've shipped ML to production alongside data scientists: built the feature pipelines, deployment path, and monitoring for models that served live decisions, not just handed off a table
Fluency in where ML breaks in the data: leakage, point-in-time joins, train/serve skew, drift
Deep SQL and Python; production dbt with tests and incremental models you actually maintained
Strong dimensional modeling — SCD Type 2, incremental loads, backfills, late-arriving data
Cloud warehouse depth. We run BigQuery and GCP;
Comfortable with orchestration, containers, and CI/CD — you can get a service deployed and keep it observable without being an infrastructure specialist
Clear communicator with strong ownership instincts — you say what you'll own, what you won't, and by when
What You'll Get In Return
A dynamic and engaging environment focused on fostering real growth and innovation
Opportunities to create amazing products that our customers truly love and value
Competitive salary with ample opportunities for career advancement and development

Nice-to-haves

Bonus: feature store or model registry experience; LLM evaluation and cost management; pricing or valuation systems; a real interest in trading cards and collectibles

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mlci/cdllmsqlpythongcp

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Health Insurance

401(K)

Wellness