Data Analyst / August, 2025 — Present

Spearheaded the design of the "ER Command Center" a Power BI solution enabling real-time resource allocation for a Level 1 Trauma Center. Designed an interactive Power BI dashboard visualizing ER patient throughput across 4 operational zones using Sankey Charts and heatmaps to pinpoint bottlenecks contributing to a 12% reduction in average door-to-doc time. Developed optimized SQL views in Snowflake performing complex joins across Admissions Lab and Staffing tables to calculate real-time KPIs like LWBS (Left Without Being Seen) rates. Engineered reusable data models with conformed dimensions (facility zone triage level provider shift) enabling consistent drilldowns and cross-team self-serve analytics. Implemented robust data-quality checks (missing timestamps duplicates negative durations) and reconciliation logic improving data trust and reducing manual validation effort. Wrote advanced SQL using DATEDIFF and CASE logic to compute operational metrics like Length of Stay (LOS) triage-to-provider and lab turnaround time enabling faster root-cause analysis. Optimized performance by consolidating heavy transformations into a single materialized view in Snowflake ensuring fast Power BI refresh and reliable near-real-time reporting. Built a lightweight Python (Pandas) automation to generate weekly Expected Volume baselines from multi-year history publishing outputs to Azure Blob Storage for benchmarking live performance. Engineered DAX measures for dynamic time-intelligence comparisons (e.g. Same Hour Last Week rolling averages) enabling leaders to spot surges and adjust staffing ratios proactively. Created a bottleneck attribution layer tagging delays to constraints (rooming provider coverage labimaging turnaround bed availability) driving targeted process interventions. Integrated Power Automate data-driven alerts to notify floor managers when wait time exceeded 45 minutes enabling rapid escalation and real-time capacity management.

Business Inventory Control Analyst / January, 2024 — May, 2025

Partnered with CALIFORNIA STATE UNIVERSITY EAST BAY and built an end-to-end inventory analytics solution that detected usage anomalies reconciled source systems forecasted demand and automated KPI reporting to reduce waste and improve ordering accuracy. Built Python anomaly detection for daily inventory usage using pandas (groupby rolling quantile) and z-scoreIQR outlier rules to flag high-variance items speeding investigation into shrink over-portioning and logging errors. Developed SQL reconciliation across purchasing receiving inventory counts and service usage to reduce data mismatches by 12% and improve inventory trust for ordering decisions. Performed EDA to identify consumption patterns by itemcategorymeal period isolating variance drivers such as events menu shifts seasonality and delivery gaps. Applied seasonal decomposition + time-series forecasting to predict semesterevent demand improving stock readiness and reducing last-minute substitutionsurgent orders. Built Power BI dashboards using Power Query (MergeAppend queries data type casting Conditional Columns) and DAX (CALCULATE FILTER DIVIDE rolling 728-day measures DATEADD for time intelligence) enabling drilldowns by locationtimeitem and supporting actions that contributed to a 9% reduction in food waste. Delivered a standardized KPI framework and review cadence that aligned chefs inventory and campus leadership around a single source of truth turning operational pain points into measurable process improvements. Led requirements elicitation and stakeholder workshops with inventoryreceiving and campus leadership; produced a signed-off BRD with scope KPIs acceptance criteria and success metrics. Defined and governed a standardized KPI framework (fulfillment accuracy waste % delivery-to-shelf time) including business rules calculation logic thresholds to eliminate inconsistent reporting. Built the WBR operating cadence with an action log (ownersdates) and dashboard-driven agenda improving decision turnaround and accountability across teams. Conducted root-cause analysis on wastemismatch drivers and translated findings into SOP change requests for receiving storage rotation portion control and logging driving measurable process fixes. Executed change management by delivering KPI dictionary + playbooks training non-technical users and implementing UAT + sign-off to ensure adoption and sustained usage.

Data Analyst / March, 2018 — May, 2022

Built the Trust and Safety reporting backbone that unied multi-million agged video events into a trusted KPI single source of truth automating opsleadership visibility and cutting reconciliation eort by 40%. Built foundational BigQuery SQL pipelines to extract and reconcile multi-million daily flagged video events across copyright hate speech misinformation and child safety queues. Standardized core Trust Safety KPIs (flag volume action rate backlog review turnaround time) by implementing a governed Looker semantic layer in LookML ensuring consistent metric definitions across Ops and Policy stakeholders. Delivered Looker dashboards Explores for daily ops reviews and weekly leadership reporting; enabled drill-downs by queue LOB region and time. Identified and resolved data mismatches caused by late-arriving events and inconsistent schemas; implemented backfill logic and reconciliation checks cutting reconciliation effort by 40%. Automated scheduled refresh + data-quality validation using Python (duplicate detection null spikes timestamp gaps) and published data health indicators alongside KPIs. Created calibration and disagreement analytics that pinpointed policyrater inconsistency hotspots driving targeted retraining that lifted detection precision 18% YoY and cut audit discrepancies 22%. Designed disagreement analytics in BigQuery to compare primary reviewer decisions vs audit outcomes surfacing high-variance policy codes and regional inconsistency patterns at scale. Built governed metrics in LookML (disagreement rate overturn rate rework rate audit pass rate) to eliminate conflicting definitions across teams. Developed heatmaps and drilldowns in LookerLooker Studio by policy type geography vendor and queue to highlight hotspots requiring intervention. Applied statistical validation and variance analysis in Python to distinguish systemic policy issues from random reviewer noise and sampling effects. Partnered with Trust Safety Policy + QA leads to translate findings into calibration packs and clarified decision guidelines.