Work
Selected work across analytics, engineering delivery, workflow automation, market intelligence, and data quality improvement.
VerdaStat™ is built around practical delivery across reporting, systems, external research, and modern data operations.
Focus Areas
Filter Work
Analytics
Azure marketing analytics pipeline and BI integration
Built Azure Blob Storage for marketing data, connected BI through DirectQuery, deployed dashboards, and embedded analytics within Dynamics 365 CRM.
- Blob Storage architecture aligned to usage and access needs
- DirectQuery and embedded dashboard delivery inside CRM
- Incremental refresh and service deployment setup
Engineering
On-demand ELT flow from Dataverse to Azure SQL
Implemented a layered pipeline across landing, staging, and publish zones with ADF Data Flows and Azure SQL delivery.
- Landing, staging, and publish storage design
- ADF transformations and SQL delivery outputs
- Security-aware configuration for storage and database access
Automation
CRM intake and workflow automation
Built an intake flow that standardized records, validated data, and automated downstream fulfillment steps.
- Branching automation logic for downstream record creation
- Data standardization and validation via expressions
- Automated follow-up and fulfillment communications
Data Quality
Dataverse data model and form scripting
Designed schema, business rules, and form logic to improve data completeness, consistency, and user workflow quality.
- Custom schema and relationship design for a new data domain
- Model-driven form scripting for validation and visibility
- Improved data completeness and standardized entry patterns
Reporting
KPI reporting across operational functions
Delivered KPI dashboards across multiple functions to reduce manual reporting and improve decision turnaround.
- Cross-functional KPI definitions with stakeholder input
- Dashboard delivery across multiple departments and use cases
- Lower manual reporting effort and faster reporting turnaround
Market Intelligence
External market data collection and analysis workflow
Developed a Python-based workflow to collect, clean, and analyze external data for reporting, trend visibility, and opportunity analysis.
- Automated data extraction across multiple source pages
- Cleaning and normalization for analysis-ready datasets
- Visualization outputs for market monitoring, opportunity review, and executive decision support
Delivery Strengths
Execution that connects business needs, technical quality, and operational continuity.
VerdaStat™ delivery includes stakeholder workshops, workflow design, UAT coordination, releases, documentation, and handoff so solutions stay usable after launch.
Problem Type
Disconnected reporting
When leaders rely on inconsistent reports, manual exports, or fragmented dashboards across teams.
Problem Type
Weak data foundations
When automation, CRM, or analytics depend on incomplete records, inconsistent fields, or poor controls.
Problem Type
Delivery bottlenecks
When teams need practical implementation help to move from roadmap or concept into working solutions.
Start Building Clarity
Bring reporting, automation, and engineering work into one focused engagement.
Use this page to frame the initiative or operating problem you want to improve next.
Discuss Your Project
Share the outcome, blockers, and support you need.