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# Staff Data Engineer, ML Platform

- **Company:** Kingsford Products, Inc.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Training Data, BigQuery, Cloud Computing, Continuous Integration, Information Engineering, Dimensional Modeling, Python (Programming Language), Machine Learning, Azure Machine Learning, SQL Databases, Management of Software Versions, Large Language Models, Evaluation of Large Language Models, Entity Resolution
- **Published:** October 18, 2026
- **Apply:** https://www.careerbuilder.com/job-details/staff-data-engineer-ml-platform--dad661f8-6355-4b10-a0e0-fd10eccfd6fe

## About the Role

* 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

## Description

About CourtyardCourtyard.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 SummaryCourtyard 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 DoOwn external market data ingestion end to end - acquisition, normalization, and delivery into the warehouseOwn the canonical item model and the entity resolution that maps messy inbound records onto itBuild quality and monitoring that catches gaps and degradation before a stakeholder noticesOwn the warehouse layer (dbt modeling, tests, documentation) and set schema contracts with product engineering so upstream changes break the build, not the metricsBuild the feature and training data layer: point-in-time correct, reproducible, and consistent between training and servingPartner with data scientists to take models from prototype to production - batch and real-time scoring, versioning, CI/CD, and safe rollout and rollbackOwn model observability - input drift, prediction quality, and retraining triggers - so a degrading model gets caught by an alert, not by a bad decisionProductionize LLM-assisted pipelines where they earn their keep, with evaluation and cost controls built inSet the engineering bar for the data org and level up DS on production-path workWhat We're Looking For7+ years across data engineering, ML engineering, or ML platform work with real production ownership, at senior or staff scopeExperience taming third-party data at volume - unstable sources, schema drift, no advance noticeEntity resolution in practice, including the judgment to know when a confident match is wrongYou'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 tableFluency in where ML breaks in the data: leakage, point-in-time joins, train/serve skew, driftDeep SQL and Python; production dbt with tests and incremental models you actually maintainedStrong dimensional modeling - SCD Type 2, incremental loads, backfills, late-arriving dataCloud 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 specialistClear communicator with strong ownership instincts - you say what you'll own, what you won't, and by whenBonus: feature store or model registry experience; LLM evaluation and cost management; pricing or valuation systems; a real interest in trading cards and collectiblesWhat You'll Get In ReturnA dynamic and engaging environment focused on fostering real growth and innovationOpportunities to create amazing products that our customers truly love and valueComprehensive health insurance packages with dependent coverageCompetitive salary with ample opportunities for career advancement and developmentEnjoy the flexibility of a fully remote work environment401(k) plan with a 4% employer match to help you plan for the future$100 monthly dogfooding stipend to support trying out our products firsthandAccess to employee wellness programs designed to support your overall well-being
About CourtyardCourtyard.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 SummaryCourtyard 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 DoOwn external market data ingestion end to end - acquisition, normalization, and delivery into the warehouseOwn the canonical item model and the entity resolution that maps messy inbound records onto itBuild quality and monitoring that catches gaps and degradation before a stakeholder noticesOwn the warehouse layer (dbt modeling, tests, documentation) and set schema contracts with product engineering so upstream changes break the build, not the metricsBuild the feature and training data layer: point-in-time correct, reproducible, and consistent between training and servingPartner with data scientists to take models from prototype to production - batch and real-time scoring, versioning, CI/CD, and safe rollout and rollbackOwn model observability - input drift, prediction quality, and retraining triggers - so a degrading model gets caught by an alert, not by a bad decisionProductionize LLM-assisted pipelines where they earn their keep, with evaluation and cost controls built inSet the engineering bar for the data org and level up DS on production-path workWhat We're Looking For7+ years across data engineering, ML engineering, or ML platform work with real production ownership, at senior or staff scopeExperience taming third-party data at volume - unstable sources, schema drift, no advance noticeEntity resolution in practice, including the judgment to know when a confident match is wrongYou'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 tableFluency in where ML breaks in the data: leakage, point-in-time joins, train/serve skew, driftDeep SQL and Python; production dbt with tests and incremental models you actually maintainedStrong dimensional modeling - SCD Type 2, incremental loads, backfills, late-arriving dataCloud 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 specialistClear communicator with strong ownership instincts - you say what you'll own, what you won't, and by whenBonus: feature store or model registry experience; LLM evaluation and cost management; pricing or valuation systems; a real interest in trading cards and collectiblesWhat You'll Get In ReturnA dynamic and engaging environment focused on fostering real growth and innovationOpportunities to create amazing products that our customers truly love and valueComprehensive health insurance packages with dependent coverageCompetitive salary with ample opportunities for career advancement and developmentEnjoy the flexibility of a fully remote work environment401(k) plan with a 4% employer match to help you plan for the future$100 monthly dogfooding stipend to support trying out our products firsthandAccess to employee wellness programs designed to support your overall well-being
About CourtyardCourtyard.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 SummaryCourtyard 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 DoOwn external market data ingestion end to end - acquisition, normalization, and delivery into the warehouseOwn the canonical item model and the entity resolution that maps messy inbound records onto itBuild quality and monitoring that catches gaps and degradation before a stakeholder noticesOwn the warehouse layer (dbt modeling, tests, documentation) and set schema contracts with product engineering so upstream changes break the build, not the metricsBuild the feature and training data layer: point-in-time correct, reproducible, and consistent between training and servingPartner with data scientists to take models from prototype to production - batch and real-time scoring, versioning, CI/CD, and safe rollout and rollbackOwn model observability - input drift, prediction quality, and retraining triggers - so a degrading model gets caught by an alert, not by a bad decisionProductionize LLM-assisted pipelines where they earn their keep, with evaluation and cost controls built inSet the engineering bar for the data org and level up DS on production-path workWhat We're Looking For7+ years across data engineering, ML engineering, or ML platform work with real production ownership, at senior or staff scopeExperience taming third-party data at volume - unstable sources, schema drift, no advance noticeEntity resolution in practice, including the judgment to know when a confident match is wrongYou'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 tableFluency in where ML breaks in the data: leakage, point-in-time joins, train/serve skew, driftDeep SQL and Python; production dbt with tests and incremental models you actually maintainedStrong dimensional modeling - SCD Type 2, incremental loads, backfills, late-arriving dataCloud 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 specialistClear communicator with strong ownership instincts - you say what you'll own, what you won't, and by whenBonus: feature store or model registry experience; LLM evaluation and cost management; pricing or valuation systems; a real interest in trading cards and collectiblesWhat You'll Get In ReturnA dynamic and engaging environment focused on fostering real growth and innovationOpportunities to create amazing products that our customers truly love and valueComprehensive health insurance packages with dependent coverageCompetitive salary with ample opportunities for career advancement and developmentEnjoy the flexibility of a fully remote work environment401(k) plan with a 4% employer match to help you plan for the future$100 monthly dogfooding stipend to support trying out our products firsthandAccess to employee wellness programs designed to support your overall well-being