I build reproducible analytics projects that turn imperfect operational data into trusted models, measurable KPIs, documented controls, and business-ready reporting. My portfolio emphasizes the full analytical workflow: requirements, synthetic data generation, validation, transformation, SQL modeling, reconciliation, analysis, automated reporting, and testing.

• SQL analysis using joins, CTEs, conditional aggregation, reconciliation logic, and window functions
• Python pipelines for extraction, validation, transformation, analytics, and automated reporting
• Dimensional data models and portable SQLite analytical warehouses
• Data-quality controls covering completeness, uniqueness, domains, ranges, and referential integrity
• Operational, financial, healthcare, customer, provider, enrollment, and product analytics
• Power BI and Tableau-ready datasets with documented metrics and business rules
• Trend, variance, exception, and anomaly analysis
• Unit-tested analytical logic and reproducible fixed-seed datasets
• Lightweight, rule-based analytical narratives grounded only in calculated results

Integrates five fictional enterprise source systems into a governed SQLite warehouse. Includes source profiling, controlled data-quality exceptions, dimensional modeling, KPI analytics, monthly trends, dashboard-ready datasets, documentation, and automated reporting.

Python SQL ETL Data Profiling Data Quality Dimensional Modeling BI Reporting 12 Unit Tests

Reconciles a synthetic transaction ledger with bank activity and reporting records. Detects duplicates, timing differences, amount variances, missing references, and unmatched transactions while producing audit-support evidence and financial KPI outputs.

Python SQL Reconciliation Variance Analysis Financial Controls Audit Support 12 Unit Tests

Analyzes fictional claims, members, providers, enrollment, plans, and contracts. Applies eligibility, paid-versus-allowed, reference-integrity, and reconciliation controls before publishing claims, provider, enrollment, and financial reporting datasets.

Python SQL Claims Analytics Provider Analytics Enrollment Data Quality 11 Unit Tests

Transforms synthetic order activity into an operational star schema, KPI reporting, daily trends, transparent z-score anomaly alerts, and a computed executive brief covering revenue, volume, processing time, completion, errors, and regional performance.

Python SQL Operational Analytics Star Schema Anomaly Detection Automated Reporting 8 Unit Tests

Business requirements
        ↓
Synthetic source generation
        ↓
Profiling and data-quality validation
        ↓
Typed transformation and reconciliation
        ↓
SQLite dimensional model
        ↓
SQL and Python analytics
        ↓
Dashboard-ready datasets and automated business reports
        ↓
Unit tests and reproducibility checks

Every featured project is designed to be cloned and run locally without private credentials. The repositories include:

• clear business problems and documented analytical requirements;
• deterministic synthetic data with controlled quality problems;
• explicit quarantine, exception, and reconciliation logic;
• primary keys, foreign keys, constraints, and useful indexes;
• machine-readable CSV/JSON outputs and business-readable Markdown reports;
• automated unit tests for important transformations, controls, KPIs, and reporting logic;
• exact setup and execution commands in each README.

All highlighted repositories are independent portfolio implementations using synthetic or public-style data. They do not contain confidential employer data, PHI, private customer information, proprietary schemas, or employer source code. Any organizations, people, accounts, transactions, claims, orders, and findings represented in these projects are fictional.

Start with the four featured projects above, or view all repositories.