Shaunda Cunningham

• Designed and maintained a greenfield production-grade data platform supporting analytics, reporting, and operational use cases, scaling platform usage and data volume by 10×.
• Migrated ad-hoc and legacy workflows into dbt, introducing standardized testing, documentation, and model versioning to improve data reliability.
• Implemented data quality checks, contracts, and CI/CD workflows to prevent breaking changes and improve trust in downstream analytics.
• Partnered with Analytics, Product, and Operations stakeholders to define data requirements, validate metrics, and ensure timely, accurate delivery.
• Developed and productionized a GenAI ingestion pipeline (OpenAI) to classify unstructured student data. This automated a manual reporting workflow and improved the accuracy of student job-outcome metrics for the executive team..

• Led a Snowflake RBAC re-architecture using Terraform, delivering secure, reproducible access control for analytics and finance stakeholders.
• Architected automated data testing and contract frameworks using Python and Great Expectations, enforcing schema validation pre-ingestion and preventing downstream data quality issues.
• Spearheaded the implementation of enterprise-grade data masking policies for Personally Identifiable Information (PII) in Snowflake. Established governance standards to meet FERPA compliance, ensuring secure access for downstream analytics and financial reporting.
• Partnered with Data Science teams to architect a Databricks environment for predictive modeling, engineering robust feature pipelines
• Implemented monitoring and alerting systems using AWS CloudWatch and Datadog, ensuring proactive issue identification, debugging, triage, and resolution.

• Developed and optimized complex ETL workflows using Apache Airflow and Python. Integrated diverse data sources into Snowflake, writing custom Python scripts to handle API pagination and transformation logic.
• Engineered an automated ingestion pipeline for customer voice interactions. Utilized Python to interface with AWS Transcribe APIs, parsing JSON outputs into structured SQL tables for sentiment analysis.
• Orchestrated Docker containers on EC2 and automated cloud infrastructure provisioning with Terraform, reducing deployment time by 50%.

• Partnered with executive leadership to optimize revenue cycle management strategies. Leveraged SQL to diagnose pricing anomalies, directly contributing to a measurable revenue lift across key markets.

Focus on End-to-End AI Applications, including Retrieval-Augmented Generation (RAG), Fine-Tuning, Agentic AI, MLOps, and Guardrails.

7-month, full-time Backend Engineering program focused on MVC/RESTful design and Test-Driven Development using Ruby, Rails, and PostgreSQL