The Engineer, Quality Systems & Data serves as the primary architect of data integrity and process reliability within the cannabis technology landscape. This role is positioned at the critical intersection of software development, quality assurance, and regulatory compliance. The core function is to design, validate, and continuously improve the complex software and data systems that track every aspect of the cannabis lifecycle. In an industry defined by a fragmented patchwork of state-specific regulations and mandatory seed-to-sale tracking systems like METRC, data is the ultimate asset. A single point of data corruption or system failure can trigger product recalls, costly operational shutdowns, and the potential loss of licensure. This engineer is therefore tasked with the proactive identification and mitigation of these risks, building a resilient digital ecosystem that supports operational scale while guaranteeing compliance. Through rigorous problem solving and system assessment, this individual ensures that the flow of information is as pure and traceable as the final product itself.
The day begins by analyzing the results of an automated data reconciliation script that runs overnight. This script compares production data in the company’s Enterprise Resource Planning (ERP) system with the data reported to the state’s METRC seed-to-sale traceability platform. An alert flags a discrepancy: a 5,000-unit batch of vape cartridges was packaged yesterday, but the state system only shows a 4,500-unit transfer. This initiates an immediate problem solving sequence. The engineer queries the production database to isolate the specific batch and operator logs, reviewing timestamps and system entries to pinpoint the source of the 500-unit gap. The investigation reveals a momentary network timeout during the API call to METRC, causing one data packet to fail.
With the immediate issue identified for correction, the focus shifts to prevention. The engineer initiates a collaboration session with a software developer and a compliance manager. They perform a rapid assessment of the API integration layer. The engineer proposes a more robust error-handling protocol with an automated retry mechanism and an immediate notification system for failed data transmissions. This ensures that a simple network issue does not become a critical compliance failure in the future. Following this, the engineer documents the incident and the corrective action in the Quality Management System (QMS) to maintain a complete audit trail.
The afternoon is dedicated to a strategic, forward-looking project. The company plans to launch a new line of cannabinoid-infused beverages, which requires a new software module for tracking liquid infusion, batch homogenization, and bottling. The Engineer, Quality Systems & Data leads a Design Failure Mode and Effects Analysis (DFMEA) session. In collaboration with process engineers and software developers, they systematically map out every potential failure point in the new software design. They ask critical questions: What happens if the system allows an operator to proceed without verifying the potency of the cannabinoid distillate? What is the risk if the system miscalculates the volume-to-potency ratio, leading to an incorrectly dosed batch? For each potential failure, the team assigns a risk score and develops specific mitigation strategies, such as adding hard-coded validation checks or requiring supervisory electronic signatures for critical control points. This proactive DFMEA process designs quality and safety directly into the system before a single line of code is finalized.
The operational cycle concludes with a focus on business intelligence. The engineer finalizes a new dashboard in Tableau that aggregates and visualizes data from Certificates of Analysis (CoAs) for all raw material inputs over the past six months. The visualization reveals that a specific terpene, myrcene, shows significantly higher variance from one supplier compared to two others. This data-driven insight allows the procurement team to initiate a supplier performance assessment and work with the underperforming vendor to improve consistency. This action directly impacts the quality and sensory profile of the final product, turning quality data into a tool for strategic sourcing and product improvement.
The Engineer, Quality Systems & Data has principal ownership over three functional domains that form the digital backbone of the organization:
The Engineer, Quality Systems & Data directly influences the organization's performance across multiple strategic vectors:
| Impact Area | Strategic Influence |
|---|---|
| Cash | Prevents significant fines from state regulators by ensuring the accuracy and timeliness of all data submitted to seed-to-sale tracking systems. |
| Profits | Reduces the cost of poor quality by designing systems that prevent data entry errors, thereby minimizing the risk of batch holds, rework, or destruction. |
| Assets | Protects the company's most valuable intangible assets: its operational license and brand reputation, which are built on a foundation of data integrity. |
| Growth | Develops a scalable and modular systems architecture that can be rapidly adapted and deployed in new states, accelerating multi-state expansion efforts. |
| People | Empowers operational staff with reliable, easy-to-use software tools, reducing manual data entry, minimizing human error, and improving job satisfaction. |
| Products | Leverages business intelligence to drive product consistency, using data to identify and correct process variables that impact cannabinoid potency, terpene profiles, and product safety. |
| Legal Exposure | Creates a defensible, time-stamped, and immutable electronic audit trail for every gram of product, significantly mitigating liability in the event of a product recall or legal challenge. |
| Compliance | Automates the collection and reporting of compliance data, ensuring that all internal processes are executed in alignment with documented and approved SOPs. |
| Regulatory | Designs flexible and configurable systems capable of adapting quickly to changes in state-level regulations regarding testing, labeling, and reporting without requiring a complete system overhaul. |
Reports To: This position typically reports to the Director of Technology or the Head of Quality & Compliance, reflecting its dual focus on technical systems and regulatory adherence.
Similar Roles: This specialized role combines elements of several traditional technology and quality positions. Professionals with titles such as Validation Engineer, Systems Analyst, Data Engineer, or Business Intelligence Developer in the pharmaceutical, medical device, or food and beverage industries possess highly relevant skill sets. The role is functionally equivalent to a Computer System Validation (CSV) Lead in a GxP environment, but with the added complexity of navigating disparate state-level cannabis regulations. Within the technology sector, this position aligns with a Technical Product Manager or a Solutions Architect focused on regulated industries.
Works Closely With: This position requires deep collaboration with the Software Development Manager, Director of Operations, Lab Director, and Chief Compliance Officer.
Proficiency with a specific technology stack is essential for operational effectiveness:
Candidates from other highly regulated, data-intensive industries are exceptionally well-positioned for success:
The role demands a unique blend of technical acumen and strategic thinking:
The standards and requirements governing this role are shaped by these key organizations:
| Acronym/Term | Definition |
|---|---|
| API | Application Programming Interface. A set of rules and protocols that allows different software applications to communicate with each other. |
| CSV | Computer System Validation. The documented process of assuring that a computerized system does exactly what it is designed to do in a consistent and reproducible manner. |
| DFMEA | Design Failure Mode and Effects Analysis. A systematic method to identify potential failures in a system design before the product is released. |
| ERP | Enterprise Resource Planning. Centralized software used to manage day-to-day business activities such as accounting, procurement, project management, and manufacturing. |
| GAMP | Good Automated Manufacturing Practice. A set of guidelines for manufacturers and users of automated systems in the pharmaceutical industry. |
| GxP | Good Practice. A general term for quality guidelines and regulations (e.g., GMP, GLP, GDP). |
| IQ/OQ/PQ | Installation Qualification / Operational Qualification / Performance Qualification. The sequential phases of computer system validation. |
| LIMS | Laboratory Information Management System. Software used in analytical labs to manage samples, tests, results, and reporting. |
| METRC | Marijuana Enforcement Tracking Reporting Compliance. A widely used state-mandated seed-to-sale software tracking system. |
| QMS | Quality Management System. The formal system that documents processes, procedures, and responsibilities for achieving quality policies and objectives. |
| S2S | Seed-to-Sale. The business process and regulatory requirement of tracking the entire lifecycle of a cannabis product. |
| SQL | Structured Query Language. A standard language for managing and manipulating data in relational databases. |
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