Jennifer McCann

A personal analytics tool built to solve a real problem: finding the right neighborhood for a growing family by meeting geographically between two sets of parents. Built end-to-end solo — scraping, SQL, Python geocoding, and a multi-page Power BI report with live filters and interactive maps.

• Scraped real estate listings (Realtor.com), school ratings (GreatSchools), neighborhood coordinates, and crime data via automated Power Automate flows, bulk loaded into SQL with custom cleaning queries.
• Wrote Python reverse geocoding script using Google Maps API to assign zip codes to raw lat/long crime coordinates.
• Built a full mortgage estimator in DAX: P&I formula (6.97% rate, 20% down, 30-year term) + monthly property tax + insurance estimate, driven by county-level tax rates for Philadelphia, Montgomery, and Delaware Counties.
• Designed 5 dashboard pages: Overview, Neighborhood, Schools, Real Estate, and Single Neighborhood deep-dive view.

Third-party software spend and usage tracking across the Customer Success organization.

• Designed the SQL data model.
• Designed and implemented the dashboard.
• Collaborated with Org Leaders to drive actionable changes.

Overview of customer health, account movement from healthy to unhealthy, and top critical alerts.

• Connected to existing data model.
• Designed and implemented the dashboard.
• Collaborated with the Global Renewals Team to define and establish KPIs.

Executive view of customer sentiment and feedback trends, delivered directly to the CEO Executive Board.

• Compiled data through a collaborative effort leveraging insights from multiple executive feedback.
• Designed and implemented the dashboard in SAC.

Executive-facing dashboard giving leadership real-time visibility into portfolio performance, delivery health, investment allocation, and strategic alignment.

• Designed end-to-end from data model to dashboard with no existing blueprint.
• Built on governed SQL views in SAP Datasphere surfacing KPIs across multiple portfolio verticals.
• Partnered directly with executives to define metrics and reporting structure.

A 0–100 portfolio health score computed in SQL across four data quality dimensions, surfaced in SAP Analytics Cloud with band-based indicators and per-owner remediation lists.

• Designed the scoring formula and all four dimension weights from scratch.
• Engineered the SQL logic in SAP Datasphere/HANA to compute scores at portfolio scale.
• Surfaced scores in SAC with color-coded bands (Excellent / Good / Needs Attention / Critical).

-- Hygiene flag: concatenates every failing condition per active feature
TRIM(
    CASE WHEN target_release IS NULL OR target_release = ''
         THEN 'Missing Target Release; ' ELSE '' END
    ||
    CASE WHEN investment_cluster_theme IS NULL OR investment_cluster_theme = ''
         THEN 'Missing Investment Cluster/Strategic Theme; ' ELSE '' END
    ||
    CASE WHEN link_flag IS NULL OR link_flag = ''
         THEN 'Missing Increment, Capacity Contribution, or Portfolio Epic; ' ELSE '' END
    ||
    CASE WHEN target_release IN (<past_release_list>)
         THEN 'Past Target Release while still active; ' ELSE '' END
) AS hygiene_flag,

-- Hygiene score: 100 minus 20 pts per failing dimension; closed items excluded
CASE
    WHEN status IN ('Obsolete','Done','Complete','Completed','Rejected') THEN 0
    ELSE 100
        - CASE WHEN target_release          IS NULL OR target_release          = '' THEN 20 ELSE 0 END
        - CASE WHEN investment_cluster_theme IS NULL OR investment_cluster_theme = '' THEN 20 ELSE 0 END
        - CASE WHEN link_flag               IS NULL OR link_flag               = '' THEN 20 ELSE 0 END
        - CASE WHEN target_release IN (<past_release_list>)                      THEN 20 ELSE 0 END
END AS hygiene_score

Demand tracking dashboard built on the intake system, providing real-time visibility into intake volume, status distribution, and routing by vertical and component.

• Engineered SQL views in SAP Datasphere to normalize the intake system data by component and vertical.
• Built intake volume, aging, and status KPIs from scratch with no existing data model.
• Automated data refresh to ensure real-time demand visibility without manual intervention.