Evidence behind the outcomes.

Judgment in practice

I traced Finance–IT friction to reporting flexibility and consistent calculations, then designed a shared process that both departments adopted. Finance defines requirements and the star-schema model; IT reviews, implements, and tests the data; I build reports from validated tables.

Delivery and adoption

I connected Revenue Actuals to the quarterly CFO dashboard and added quarter-to-date pacing, order-status, and shipped-not-invoiced views to support Finance forecasting reviews.

I gave Sales pricing comparisons across years, regions, and customers to assess discount patterns. Average selling price logic excluded returns and RMAs; colleague feedback estimates approximately 2 hours saved per period comparison.

For the same OpEx reporting work, colleague feedback also estimates a 20% reduction in manual reporting time through improved invoice validation and cost-centre visibility. This estimate may overlap with the 10+ review hours per cycle.

I helped design the sales-region model, prepared Salesforce region data, and contributed to reporting on sales trends and commission calculations. I linked D365 and Salesforce data to identify end customers behind distributor sales and implemented that mapping in Power BI.

I identified customer-address errors that could misattribute sales, initiated cleanup, and worked with Salesforce and D365 leads to implement controls. I also built product pull-through reporting that maps system sales and nearby consumables demand to help Sales explore opportunities and sales declines.

The commercial reporting dataset covered approximately 2 million rows, three years of history, 150 customers, and more than 1,000 SKUs. These figures describe the dataset, not company totals.

I delivered an executive insights AI agent for the CFO, CEO, and regional leaders, with business-user use confirmed. Parameterized DAX, structured responses, access restrictions, and regression tests are functional. I also built D365 query-support and Power BI ticket-intake agents; their adoption has not been measured.

I evaluated Microsoft Fabric’s Power BI Copilot, Dataflow Gen2, deployment pipelines, XMLA endpoints, data-lake integration, Git-based version control, and workload/capacity needs. I created a capacity-testing roadmap and success criteria and applied ALM Toolkit, Tabular Editor, and selective partition refreshes during the trial. This remains evaluation and trial work, with no completed production migration or measured efficiency gain claimed.

I also helped improve collaboration between Finance in Toronto and Accounting in Singapore by clarifying data requirements, surfacing concerns, and aligning stakeholders on shared reporting needs.

I queried data-lake and Salesforce-sourced data using SQL/SSMS to support Finance reporting.

I automated recurring month-end ERP data handling so the required information could reach reporting with less manual intervention.

Judgment in practice

I aligned PMO, business, and data-engineering stakeholders while setting team priorities and reviewing quality. Development standards and training helped the team reuse the capability.

Delivery and adoption

I translated federal-program reporting requirements into Power BI and paginated reports, aligning business, PMO, and Data Engineering. I queried Databricks data with SQL and connected it to Power BI for reporting.

I used ALM Toolkit, Tabular Editor, and selective SSMS partition refreshes to avoid full-refresh bottlenecks in large incremental models. The approximately 4-hours-to-minutes result applies to that deployment and refresh workflow.

I also helped design the model columns needed for incremental refresh and moved calculations into the semantic layer to improve efficiency. This collaborative model-design work has no separate measured time-saving result.

I coached analysts in data modeling, transformation logic, and reporting, and created paginated-report standards and training adopted across the team. I resolved demanding design constraints and identified a defect later acknowledged by Microsoft.

As one of Canada Life’s five Power BI Champions, I provided organization-wide training and consulting support for complex reporting and delivery issues.

Judgment in practice

I independently initiated the dropped-claims investigation and developed solution options, validating the records with claims specialists and stopping affected loads. I presented options and trade-offs to the COO and led a hotfix within one week. The financial exposure was potential.

Delivery and adoption

During a major data load, I identified legitimate claims being dropped by the orchestration engine. I independently initiated the investigation and developed solution options, validated examples with claims specialists, stopped affected loads, and presented the trade-offs to the COO. I led a hotfix implemented within one week; the issue represented potential multimillion-dollar impact.

I managed direct reports within a four-person analytics support function, assigning work across data validation, stakeholder complaints, and analytical requests while rebuilding trust with business partners affected by data-quality issues.

I mentored team members and held occasional one-on-ones to help solve problems, review their work and day-to-day performance, and offer feedback and encouragement.

I redesigned onboarding methods and training materials, reducing onboarding from four weeks to two. Within my first year on P34, I could provide business-knowledge training despite joining without an insurance background.

After the original vendor exited, I assumed responsibility for maintaining and improving the orchestration engine based on my end-to-end knowledge of the ETL process, transformation rules, testing, and business requirements.

I introduced temporal tables for current-record processing and point-in-time queries and implemented checks for missing key data and invalid date relationships. Rule-based matching controls could hold processing and draft an email for analyst review, awaiting a correction or manager override.

I contributed to client SQL database models and co-designed the Power BI data and semantic layers with Data Engineering, finalizing a workable star schema and owning the reporting layer. I also queried data-lake and Salesforce-sourced data using SQL/SSMS.