Seeking a seasoned Fraud & Cyber Data Intelligence Engineer with strong data engineering and analytical capabilities to support our Fraud and Cyber Risk organization. In this role, you will be instrumental in unlocking the full value of Splunk to expose fraud indicators, cybersecurity weaknesses, behavioral patterns, and emerging threat typologies—ultimately delivering insights that reinforce our protective strategies.
Interview Timeline: December, ideally prior season holidays
Interview Process: case study to complete in person at the office (core downtown Toronto)
Start Date: early January
Initial Term: 6-month contract with a strong likelihood of extension
Location: Downtown Toronto
Hybrid Model: 3-4 days per week onsite (mandatory/ need to be in the office given that this is a fraud/security role)
Key Qualifications:
- Expert experience with Splunk
- Experience working with fraud or cybersecurity analytics
- Strong scripting experience with Python and SQL for advanced data manipulation
- Experience working with Event Source Kinesis and Dinamo
- Experience working with data that has a Digital entry point - must have **
- Ability to understand and test Fraud hypothesis among data and recognize what data would trigger the hypothesis
Requirements:
Technical Skillset:
- Advanced command of Splunk (complex queries, dashboards, alerts, and metadata interpretation) — true expert-level proficiency required.
- Strong grasp of Splunk architecture, data ingestion, and pipeline design.
- Proven experience integrating Splunk with external data sources (Excel, APIs, and more).
Analytical Capabilities:
- Demonstrated ability to identify anomalies, behavioral patterns, and typologies within large datasets.
- Problem-solver with a focus on mitigating fraud and cyber threats.
Preferred Background:
- Experience in fraud analytics, cybersecurity, or risk-focused roles.
- Working knowledge of Python, SQL, or similar scripting languages for advanced data handling.
Primary Responsibilitie s
Expert-Level Splunk Engineering
- Architect, refine, and maintain sophisticated Splunk searches, visualizations, and alerting mechanisms to derive meaningful intelligence from extensive and varied data sources.
- Leverage deep knowledge of Splunk metadata to reveal subtle or hidden indicators of fraud and cyber activity.
- Build Splunk-driven monitoring and anomaly-detection capabilities that enable early identification of threats.
Data Engineering & System Integration
- Create unconventional and efficient data pipelines that blend Splunk outputs with Excel and additional data sources.
- Develop scalable ETL routines to support analytics across fraud detection and cyber risk domains.
Fraud & Cyber Threat Analysis
- Examine large datasets to uncover new fraud schemes, attack vectors, and structural vulnerabilities.
- Deliver actionable recommendations to enhance controls, strengthen defenses, and address identified risks.
- Partner closely with fraud, cybersecurity, and risk teams to translate intelligence into operational processes.
Innovation & Continuous Improvement
- Keep current with evolving threat landscapes, Splunk enhancements, and data engineering techniques.
- Champion new methods for applying Splunk and metadata analytics to advance our detection and prevention strategies.
