Senior Analytics Advisor

We are is seeking an accomplished Senior Advisor, Analytics to translate complex operational and financial data into strategic insights that directly influence pipeline performance, safety, regulatory compliance, and business unit financial outcomes. Serving as a senior technical authority, this individual will bridge pipeline operations, advanced analytics, and AI-enabled decision support. The role leverages modern enterprise platforms (notably Databricks and Azure Cloud), advanced programming in Python and SQL, and Copilot-based agent workflows to drive automated insight generation, predictive scenario modeling, and operational intelligence across major liquid pipeline networks in Canada and the United States.

Core Responsibilities

• Lead Advanced Analytical Initiatives: Architect and execute complex data solutions using Python, SQL, and Databricks to transform operational, financial, and market data into decision-ready strategic intelligence.
• Engineer Data Pipelines & Govern Assets: Collaborate with cross-functional engineering teams to implement robust data engineering practices on Azure Cloud, adhering to Unity Catalog governance standards and maintaining version control via Git/CI-CD pipelines.
• Deploy AI & Machine Learning Solutions: Develop, operationalize, and maintain AI/ML models on Databricks, including the deployment of Copilot-based agents to automate recurring analyses, detect anomalies, explain core metrics, and scale scenario modeling.
• Model Financial & Operational Impact: Conduct rigorous economic and sensitivity analyses - including revenue impacts and marginal cost evaluations - to inform capital allocation, throughput optimization, and capacity utilization.
• Synthesize Operational Data: Ingest and analyze near-real-time industrial telemetry (e.g., AVEVA PI System, SCADA-derived data) to engineer leading KPIs, evaluate risk controls, and enhance asset reliability.
• Drive Executive Advisory & Cross-Functional Alignment: Serve as a strategic liaison between technical teams and executive business units - such as Finance, System Performance, Strategy, and Investor Relations - translating complex algorithmic metrics into actionable executive briefings.
• Provide Technical Leadership: Deliver technical mentorship, establish best-practice documentation for analytics and AI workflows, and elevate engineering standards across junior and intermediate personnel.

Required Qualifications

• Education: Bachelor’s degree in Engineering, Data Analytics, Computer Science, Finance, Economics, or a related quantitative discipline.
• Industry Experience: Minimum of 7 years of progressive experience within pipeline operations, midstream energy, or high-consequence operational analytics environments.
• Data Engineering & Cloud Platforms: Demonstrated hands-on expertise building production data pipelines and analytical workflows on Microsoft Azure Cloud and Databricks.
• Programming & Governance: Advanced proficiency in Python and SQL; working experience with Git version control and CI/CD pipelines; familiarity with enterprise data governance (Unity Catalog).
• AI & Advanced Analytics: Practical experience operationalizing AI/ML workloads on Databricks and leveraging AI-assisted analytics or Copilot agents for metric automation and scenario forecasting.
• Operational Systems & Tooling: Proven ability to query and analyze industrial time-series or SCADA-derived datasets (e.g., AVEVA PI System); proficiency in developing executive dashboards using Power BI and leveraging advanced Excel/VBA modeling where required.
• Executive Communication: Exceptional ability to synthesize complex mathematical, financial, and operational dependencies into concise presentations and strategic recommendations for senior stakeholders.

Preferred Qualifications

• Domain Knowledge: Deep subject matter expertise in liquid pipeline operational drivers, including throughput modeling, capacity utilization, power/drag optimization, and regulatory compliance metrics.
• Professional Certifications: Industry-recognized credentials, such as Databricks Certified Data Engineer, Microsoft Certified: Azure Data Engineer Associate, or Certified Analytics Professional (CAP).