About the Team Engineers on this team build our rules-based calculation engine for processing sales ... Experience with rule-driven systems, validation workflows, calculation engines, or approval ...
About the Team Engineers on this team build our rules-based calculation engine for processing sales ... Experience with rule-driven systems, validation workflows, calculation engines, or approval ...
About the Team Engineers on this team construct our rules-based calculating engine for processing ... Java/Springboot, Django, Postgres Infrastructure: AWS, Docker What success looks like: 30/60/90 ...
About the Team Engineers on this team construct our rules-based calculating engine for processing ... Java/Springboot, Django, Postgres Infrastructure: AWS, Docker What success looks like: 30/60/90 ...
Director, Payments Technology
Toronto, ON · On-site +1
Owning the evolution of the enterprise fraud engine including real time scoring, decisioning ... CSS/HTML, React / Angular, Azure Web App Services, Java & JavaScript, Python, SQL/databases, REST ...
Director, Payments Technology
Toronto, ON · On-site +1
Owning the evolution of the enterprise fraud engine including real time scoring, decisioning ... CSS/HTML, React / Angular, Azure Web App Services, Java & JavaScript, Python, SQL/databases, REST ...
Java Drools Rule Engine information
What is the difference between Java Drools Rule Engine vs Java Business Analyst?
| Aspect | Java Drools Rule Engine | Java Business Analyst |
|---|---|---|
| Primary Role | Designing and implementing business rules using Drools | Analyzing business processes and requirements |
| Required Skills | Java, Drools, rule-based systems | Business analysis, Java, communication skills |
| Work Environment | Software development teams, IT projects | Business units, project management |
| Industry Usage | Financial, insurance, healthcare IT systems | Business process improvement, system implementation |
Java Drools Rule Engine developers focus on creating and maintaining rule-based systems using Drools, requiring technical Java skills. Java Business Analysts analyze business needs and translate them into technical requirements, often collaborating with developers. While both roles may work in similar industries, their core responsibilities and skill sets differ significantly.
What are the key skills and qualifications needed to thrive as a Java Drools Rule Engine Developer, and why are they important?
To excel as a Java Drools Rule Engine Developer, you need strong Java programming skills, a solid understanding of business rule management systems (BRMS), and experience with rule authoring and testing. Familiarity with the Drools framework, rule syntax (DRL), integration tools like KIE Workbench, and knowledge of related technologies such as Maven or Spring are typically required. Analytical thinking, attention to detail, and effective communication are crucial soft skills to translate complex business logic into executable rules and collaborate with stakeholders. These competencies ensure accurate implementation of business rules, efficient system performance, and alignment with organizational objectives.
What are some common challenges faced when implementing business rules using the Java Drools Rule Engine, and how can they be addressed?
One common challenge is managing the complexity of large rule sets, which can become difficult to maintain and debug as business logic evolves. To address this, it's important to establish clear documentation and naming conventions, modularize rules into logical groups, and use version control effectively. Collaboration with business analysts is also key to ensuring rules accurately reflect requirements. Additionally, thorough testing—both unit and integration tests—helps catch issues early and ensures rules interact as expected.
What is a Java Drools Rule Engine?
A Java Drools Rule Engine is a business rules management system (BRMS) written in Java that allows developers to define, deploy, execute, and maintain business rules separately from application code. Drools uses a declarative approach, enabling subject matter experts to specify rules in a readable format, which the engine then interprets and enforces at runtime. This separation of rules from logic helps organizations adapt to changing business requirements more efficiently. Drools supports complex event processing, workflows, and decision tables, making it versatile for various business scenarios.
What are popular job titles related to Java Drools Rule Engine jobs in Toronto, ON? For Java Drools Rule Engine jobs in Toronto, ON, the most frequently searched job titles are:
What job categories do people searching Java Drools Rule Engine jobs in Toronto, ON look for? The top searched job categories for Java Drools Rule Engine jobs in Toronto, ON are:
Other
Re-posted 13 days ago
Job description
About the Team
Engineers on this team build our rules-based calculation engine for processing sales commissions. This might sound simple if you have never been exposed to sales compensation plans, it is not.
We are low on meetings and high on accountability. Most of the team is in the EST time zone, with a few located in PST and Central as well. We are still evolving many areas of the platform, which means there is meaningful room to improve the design, reliability, and scalability of the systems we build.
What you'll be doingReporting to the Manager of Data Platform, you will play an important role in the evolution of our Spark-based data platform. You'll design and build data-rich platform capabilities, contribute to system design discussions, and help ensure our data systems remain reliable, maintainable, and scalable as Forma grows.
As a Senior Engineer, Data Platform, you are expected to operate with strong ownership and sound technical judgment. This includes identifying risks in the work you own, surfacing edge cases, asking thoughtful questions, and proposing improvements that strengthen the quality and reliability of the platform.
You will:
- Design, build, and improve Spark-based data pipelines and platform services.
- Work with complex data models representing sales compensation plans, hierarchies, relationships, and enterprise datasets.
- Build reliable, deterministic data systems that customers and internal teams can trust.
- Improve testing, observability, data quality, and production reliability across the systems you work on.
- Partner with Product, Engineering, and Analytics to translate complex business requirements into scalable data designs.
- Participate in design reviews, code reviews, technical discussions, and knowledge sharing.
- Use AI tooling to improve delivery speed while maintaining strong engineering standards.
- Strong experience building data systems or backend systems in production.
- Experience with Spark or similar data processing / ETL frameworks.
- Proficiency in at least one production-grade language such as Python, Java, Scala, Kotlin, Go, C#, or similar.
- Strong SQL, relational schema design, and data modeling skills.
- Experience with large-scale, hierarchical, graph-like, relationship-heavy, or workflow-driven datasets.
- Ability to reason through technical tradeoffs, identify risks, and propose practical improvements.
- Experience improving scalability, reliability, observability, or maintainability in data-intensive systems.
- Strong communication skills and comfort collaborating across Engineering, Product, and Analytics.
- Experience building SaaS products for mid-market or enterprise customers.
- Experience with rule-driven systems, validation workflows, calculation engines, or approval/governance platforms.
- Familiarity with AWS-based infrastructure and Kubernetes.
- Familiarity with graph databases or graph-based modeling concepts.
- Exposure to Sales Performance Management, RevOps, Incentive Compensation, or related domains.
Frontend: JavaScript, React, TypeScript
Backend: Java/Spring Boot, Django, Postgres
Data Platform: Spark
Infrastructure: AWS, Docker
What success looks like: 30/60/90 daysFirst 30 daysYou'll focus on building context across Forma's product domain, data platform, calculation engine, data models, and engineering practices.
By the end of your first 30 days, you will have:
- Set up your development environment and become comfortable navigating the codebase, data platform, services, and infrastructure.
- Built a clear understanding of the product domain, Spark-based data flows, and key data models.
- Learned the team's practices around testing, observability, deployment, data quality, and reliability.
- Built relationships with Engineering, Product, and Analytics partners.
- Shipped small improvements or fixes to build familiarity with the system.
You'll begin owning meaningful data platform work and contributing to technical decisions.
By the end of your first 60 days, you will have:
- Taken ownership of a data pipeline, platform component, workflow, or feature area.
- Designed and delivered maintainable data platform code aligned with team standards.
- Partnered with Product, Engineering, and Analytics peers to translate requirements into scalable data designs.
- Identified risks, edge cases, data quality issues, or inconsistencies in the systems you work on.
- Contributed to improvements in data modeling, pipeline reliability, testing, observability, or service boundaries.
You'll be operating as a trusted senior contributor within the team.
By the end of your first 90 days, you will have:
- Designed and delivered a meaningful data platform initiative or major feature.
- Improved important data models, Spark pipelines, platform services, or workflow handling.
- Contributed to technical direction through clear, well-reasoned design decisions.
- Improved the reliability, observability, scalability, or maintainability of data-intensive systems.
- Helped the team deliver complex calculation and data workflows with greater confidence and clarity.
Additional Job Info:
- This position is for an existing vacancy
- Salary Range: 150-190K CAD