Pay Range: $35.00hr - $40.00hr

Job Overview:

As a motivated self-starter with a passion for solving complex business and technology challenges, the candidate will play a key role in advancing the organization's Risk Observability, Measurement, Analytics, and Automation capabilities. Operating at the intersection of data engineering, analytics, risk management, observability, and emerging technologies, the successful candidate will help transform operational and risk data into actionable insights that improve transparency, decision-making, and organizational resilience. The candidate will partner closely with stakeholders across Technology Risk, Engineering, Operations, Governance, Compliance, and Technology organizations to design and implement modern analytical solutions. Through a combination of data engineering, metric development, dashboarding, automation, and AI-enabled capabilities, the individual will help provide end-to-end visibility into control coverage, control health, behavioral performance, directional trends, risk posture, and areas of risk concentration. This role is ideal for an intellectually curious individual who thrives in ambiguity, enjoys solving complex problems, communicates effectively with technical and non-technical stakeholders, and has a genuine interest in data engineering, analytics, observability, automation, artificial intelligence, and emerging technologies. The ideal candidate will combine strong technical skills with business acumen and communication capabilities, allowing them to bridge the gap between stakeholders, data, and technology solutions.

Responsibilities:

- Design, develop, and maintain scalable measurement frameworks that provide end-to-end visibility into control coverage, health, effectiveness, and behavioral performance.
- Leverage Artificial Intelligence to optimize architecture and improve automation.
- Engineer observability solutions that proactively monitor configuration changes, control performance, operational trends, risk signals, and testing outcomes.
- Continuously assess risk posture against defined tolerance thresholds and identify areas that are within limits, approaching breach, or out of tolerance.
- Identify directional trends across control populations including improvement patterns, degradation signals, and early warning indicators.
- Expose relationships between controls, technology assets, processes, and levels of criticality to identify where risk is concentrated and where failures may have the greatest impact.
- Acquire, transform, enrich, validate, and reconcile data used within reporting, measurement, and control observability frameworks.
- Develop lightweight integrations and automated data acquisition approaches across enterprise platforms,



internal datasets, and operational data sources.
- Engineer, implement, and maintain Key Risk Indicators (KRIs), Key Control Indicators (KCIs), Key Performance Indicators (KPIs), scorecards, and supporting measurement methodologies.
- Design scalable metric architectures capable of supporting large populations of controls, technology assets, operational processes, and risk reporting use cases.
- Translate stakeholder requirements into business rules, calculation logic, control measurements, analytical models, and repeatable reporting outputs.
- Develop analytical models, dashboards, scorecards, executive reporting, and observability views that steer business decisions and improve risk management outcomes.
- Analyze complex datasets to identify trends, anomalies, emerging risks, data integrity concerns, and opportunities for operational improvement.
- Automate KRI reporting processes, governance deliverables, dashboard observability, risk scorecards, and ticket workflow automation where appropriate.
- Support Risk Control Observability reporting initiatives, scorecard development, stakeholder roadshows, and alignment with governance forums.
- Identify opportunities to automate manual operational processes, legacy scripts, reporting activities, workflow handoffs, and analytical procedures.
- Leverage Python, SQL, Snowflake, Power Platform, Power Automate, repositories, SDLC practices, and source control.
- Utilize GitHub Copilot and AI-assisted development to deliver scalable solutions.
- Research and apply Generative AI, Cortex Platform capabilities, AI skills development, and emerging technologies to improve workflow automation, proactive risk identification, and capability enhancements.
- Gather, document, and refine business requirements through direct stakeholder engagement, structured problem solving, and solid communication.
- Translate ambiguous business challenges into actionable analytical engineering, automation, and reporting solutions.
- Support product ownership, squad delivery, program leadership, roadmap development, prioritization, horizontal strategy, and executive-level presentations.

Qualifications and Skills Required:

- 5+ years of experience in Analytics Engineering, Data Engineering, or Business Intelligence.
- 2+ years using AI Development Tools and Frameworks.




- Strong problem-solving and critical-thinking capabilities with the ability to decompose complex challenges into practical, scalable solutions.
- Demonstrated ability to learn new technologies, business domains, platforms, and analytical methodologies quickly.
- Excellent verbal and written communication skills with the ability to engage both technical and non-technical stakeholders.
- Experience soliciting requirements from stakeholders and translating business objectives into measurable technical outcomes.
- Ability to work in complex, fast-paced, and ambiguous environments while managing multiple priorities and driving items to completion.
- Strong stakeholder management, relationship-building, organization, and execution skills.
- Passion for data analytics, engineering, observability, automation, artificial intelligence, and emerging technologies.
- Interest in Technology Risk Observability, Monitoring, Operations, Compliance, Security, Control Effectiveness, or related disciplines.

Technical Skills:

- Data Engineering & Integration: SQL, Python, Snowflake, ETL/ELT Development, API Integration, Data Quality Management.
- Analytics & Measurement Engineering: KPI, KRI, and KCI Development, Risk Analytics, Trend Analysis, Dashboard Development, Power BI, Executive-Level Reporting, Observability Analytics, Control Effectiveness Analysis.
- Development Tooling & Artificial Intelligence: Repositories, SDLC, Source Control, GitHub Copilot, AI-Assisted Development, Generative AI Technologies, Power Platform, Copilot Studio, Cortex Platform.
- Automation: Workflow Development, Power Platform, Power Automate, workflow automation, monitoring and alerting automation, process optimization, legacy script automation, and maintenance.

Preferred Qualifications:

- Experience in Technology Risk, Operational Risk, Compliance, Audit, Security Controls, Governance, Observability, Monitoring, Operations, or related functions.
- Experience building enterprise-scale measurement, scorecard, observability, and risk intelligence capabilities.
- Familiarity with OpenPages, control frameworks, governance reporting processes, compliance analytics, encryption reporting, cloud observability, or asset governance initiatives.
- Experience identifying emerging risks, expanding KRI/KCI/KPI measurement coverage, supporting risk testing, evaluating control effectiveness, and developing early warning indicators.
- Experience with scenario analysis, stress testing, risk forecasting, proactive monitoring, alerting, production support, and operational workflow automation.
- Strong interest in leveraging analytics, automation, artificial intelligence, and emerging technologies to create measurable business value.

📌 Observability and Analytics Engineer (Montreal)
🏢 Cynet Systems
📍 Montreal