Data Engineer - Food Cost Analytics

About the role

Food cost sits at the intersection of purchasing, prep, portioning and menu mix, and is one of the largest line items in the business. The company is hiring a data-minded engineer to trace where margin leaks occur, quantify why they happen and ship the code that helps teams fix them. This is not a reporting-only role: you will own the full loop from data diagnosis to production deployment.

Key responsibilities

• Analyze purchase orders, vendor invoices, inventory counts and point-of-sale data to find drivers of food cost variance by store, vendor, item and week.
• Write and maintain MySQL and SQL Server queries that surface waste, over-prep, portion drift, price movement and invoice discrepancy trends.
• Build and support the data feeds, calculations and fixes behind the internal food-cost tooling.
• Write and deploy production PHP code in internal applications so food-cost insights flow into the workflows used by store and operations teams.
• Detect data quality issues such as missing items, mismatched units of measure and duplicate or stalled imports, and work with engineering to resolve them.
• Communicate findings clearly in writing, stating what the data shows, how confident we are and what action it recommends.
• Partner with purchasing, store operations and leadership to turn analytical findings into measurable cost reductions.

Required profile

• Demonstrated analytical ability: turning ambiguous business problems into testable questions and answering them with data.
• Clear, concise written communication, comfortable writing up findings for non-technical audiences.
• Comfort working directly with stakeholders to turn analysis into action.
• Production experience writing and deploying PHP in a real application (preferred but not essential).

Required skills

• Strong SQL across MySQL, SQL Server and BigQuery, including joins, aggregations, windowed logic and performance-minded query design on transactional data.
• Working knowledge of relational database design and data quality diagnostics (duplicates, nulls, grain mismatches, unit conversions).
• Ability to use LLMs effectively and quickly.
• Preferred: familiarity with food-cost concepts (theoretical vs actual food cost, yield testing, plate cost, inventory valuation), vendor invoice reconciliation or purchase-order auditing, reporting tools such as Quicksight, and POS or ERP data integration (Brink, SQL Server, BigQuery).