Dustin Meyer

About Me

Senior analytics and data management professional with 7+ years of experience delivering data-driven strategy, enterprise reporting frameworks, and modern data platform solutions. Skilled in research-driven analysis, data architecture, data governance, and thought leadership. Adept at transforming complex technical and market trends into clear, actionable insights for executives and clients. Known for strong storytelling, excellent communication, and the ability to synthesize emerging technologies such as AI/GenAI, data lakehouses, and modern data stack into pragmatic guidance. I specialize in building interactive dashboards, automating data pipelines, and creating predictive models that help organizations make data-driven decisions with confidence.

Technical Skills

Featured Projects

View Technical Documentation ↗

Colorado Rockies: Fan Retention & LTV Engine

dbt Core Snowflake Analytics Engineering GitHub

Architected an automated fan engagement pipeline to transform raw ticket transaction data into actionable Fan Lifetime Value (LTV) and Churn Risk models.

• • Developed a Predictive Churn Layer identifying at-risk fans based on 180-day and 365-day recency windows, enabling proactive win-back marketing.
• • Engineered a Fan Loyalty Segmentation engine (Super Fan, Frequent, Occasional) to drive targeted high-margin premium seating upsells.
• • Established Hardened Data Governance using dbt schema testing (Unique, Not_Null, Accepted_Values) to ensure 100% accuracy for Revenue Team reporting.
• • Implemented an A/B Testing Framework to measure revenue lift, validating a 12.8% increase in fan spend through targeted merchandise incentives.

View Technical Documentation ↗

Enterprise Retail Intelligence Pipeline

dbt Cloud Snowflake SQL GitHub

Architected an automated ELT pipeline to transform 1M+ rows of raw TPC-H retail data into a refined "Gold" layer for executive decision-making.

• • Developed a Medallion Architecture (Staging, Intermediate, Marts) in Snowflake, improving data modularity and reducing redundant logic.
• • Engineered automated Customer Lifetime Value (LTV) calculations at the transformation layer, providing a single source of truth for all downstream BI tools.
• • Implemented Data Governance through automated schema testing (Unique, Not_Null) and version-controlled documentation to ensure 100% data integrity.

View Live Dashboard ↗

US Oil Production Analysis

SQL Python Tableau

A case study on beautiful dashboard design while proving great detail at a glance.

• • Engineered an end-to-end analytics pipeline by extracting and cleaning 20 years of historical production data from Enverus to visualize long-term output trends across the global energy sector.
• • Developed a multi-tier comparative framework that segments performance by "Global Giants," "Large US Public," and "Private US" firms, allowing stakeholders to benchmark production volatility across different corporate structures.
• • Designed a high-impact monthly production heatmap to identify seasonal output patterns and historical anomalies, utilizing advanced UI/UX principles in Tableau to maintain clarity across dense, multi-company datasets.

View Live Dashboard ↗

Website Traffic Analysis

SQL Python Tableau

Turning complex datasets into visually stunning, actionable insights that drive business growth.

• • Analyzed multi-channel acquisition patterns to identify high-converting traffic sources, providing actionable insights into user behavior and navigation paths.
• • Engineered a retention-focused reporting suite to track return customer rates, utilizing cohort analysis to pinpoint the primary drivers of repeat site visits.
• • Developed interactive KPIs for session duration and bounce rates, allowing stakeholders to visualize the direct correlation between content engagement and user loyalty.

View Live Dashboard ↗

Automated Subscription Intelligence Platform

Python Snowflake Looker

Architected a scalable data pipeline to transform raw subscription logs into real-time executive insights for recurring revenue growth.

• • Engineered an automated ETL pipeline using Python and Snowflake to ingest, clean, and model high-volume subscription data, reducing manual reporting overhead by 100%.
• • Developed a centralized Looker Semantic Layer to define standardized business logic for Monthly Recurring Revenue (MRR), churn rates, and customer lifetime value (LTV).
• • Created dynamic executive dashboards that identify high-risk churn segments and expansion opportunities, enabling data-driven retention strategies across the organization.

View Live Dashboard ↗

Integrated Commerce & Operations Intelligence Hub

SQL Python Tableau

Optimizing the intersection of high-growth e-commerce sales and large-scale inventory logistics to drive enterprise-wide operational efficiency.

• • Synthesized dual-stream data architectures to provide a unified view of Hoonigan’s front-end retail performance alongside Wheel Pros’ backend inventory levels.
• • Engineered an Efficiency vs. Growth analytical model that correlates real-time SKU velocity with stock-on-hand, identifying optimal reorder points to prevent stockouts during peak promotional cycles.
• • Developed a cross-functional executive suite that visualizes fulfillment bottlenecks and warehouse turnaround times, directly impacting bottom-line profitability through reduced overhead.

Sales Performance Analytics

Power BI DAX SQL

A comprehensive Power BI suite developed to track regional sales quotas and year-over-year growth metrics.

• • Analyzed historical sales data to identify seasonal trends and underperforming territories.
• • Developed custom DAX measures for Rolling 12-Month Revenue and Variance to Budget.
• • Delivered automated weekly snapshots to sales leadership, replacing manual Excel trackers.

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