Data Engineer for Data Integration
Data Engineer
Pricing, Inventory & Payments Data
Remote (US)
About the role
We run a digital card-ripping platform: customers buy digital packs, rip them to reveal real graded cards held in a secure vault, then sell back, keep vaulted, or ship home. Every action depends on data being right; what a card is worth this minute, which physical slab maps to which digital item, and whether the money in and out reconciles to the penny.
You'll own that data backbone, working alongside a team of data scientists and financial analysts who depend on you for clean, reliable data. You'll build and run the pricing engine, the inventory-and-vault ledger, and the payments/revenue reporting the business sits on.
What you'll own
• Pricing: build and maintain the pipeline that produces a current fair-market value for every card and grade — from a licensed feed (e.g. Card Ladder) or aggregated sales comps — and catch stale or anomalous pricing.
• Inventory: keep the source-of-truth ledger tying each digital item to a real physical slab, with reconciliation so digital and physical never drift apart, plus pack composition and odds tables.
• Payments & reporting: pipe payments, payouts, fees, and chargebacks into clean reporting, and build the dashboards the data science and analyst team rely on — pack-level margin, buyback exposure, payout reconciliation, and LTV.
What we're looking for
• 3–5 years in data engineering, ideally including a financial, marketplace, fintech, or e-commerce system where correctness mattered.
• Strong Python and SQL — you model data well and write pipelines others can trust and maintain.
• Hands-on with ETL/ELT pipelines, a data warehouse (Snowflake, BigQuery, Redshift, or Postgres), and an orchestration tool (Airflow, Dagster, dbt).
• A track record of owning reconciliation or financial-accuracy work where being off by a cent is a real problem.
• Experience supporting data scientists and analysts — you understand what clean, well-modeled data they need to do their work.
Nice to have: collectibles/grading knowledge, pricing or market-data feeds, or payments infrastructure (Stripe, ACH, PayPal/Venmo), QuickBooks expertise
Apply tot his job
Apply To this Job
Pricing, Inventory & Payments Data
Remote (US)
About the role
We run a digital card-ripping platform: customers buy digital packs, rip them to reveal real graded cards held in a secure vault, then sell back, keep vaulted, or ship home. Every action depends on data being right; what a card is worth this minute, which physical slab maps to which digital item, and whether the money in and out reconciles to the penny.
You'll own that data backbone, working alongside a team of data scientists and financial analysts who depend on you for clean, reliable data. You'll build and run the pricing engine, the inventory-and-vault ledger, and the payments/revenue reporting the business sits on.
What you'll own
• Pricing: build and maintain the pipeline that produces a current fair-market value for every card and grade — from a licensed feed (e.g. Card Ladder) or aggregated sales comps — and catch stale or anomalous pricing.
• Inventory: keep the source-of-truth ledger tying each digital item to a real physical slab, with reconciliation so digital and physical never drift apart, plus pack composition and odds tables.
• Payments & reporting: pipe payments, payouts, fees, and chargebacks into clean reporting, and build the dashboards the data science and analyst team rely on — pack-level margin, buyback exposure, payout reconciliation, and LTV.
What we're looking for
• 3–5 years in data engineering, ideally including a financial, marketplace, fintech, or e-commerce system where correctness mattered.
• Strong Python and SQL — you model data well and write pipelines others can trust and maintain.
• Hands-on with ETL/ELT pipelines, a data warehouse (Snowflake, BigQuery, Redshift, or Postgres), and an orchestration tool (Airflow, Dagster, dbt).
• A track record of owning reconciliation or financial-accuracy work where being off by a cent is a real problem.
• Experience supporting data scientists and analysts — you understand what clean, well-modeled data they need to do their work.
Nice to have: collectibles/grading knowledge, pricing or market-data feeds, or payments infrastructure (Stripe, ACH, PayPal/Venmo), QuickBooks expertise
Apply tot his job
Apply To this Job