About
Data-driven problem solver with 13+ years of experience in data analytics, data engineering, and cloud services. Specialized in designing end-to-end data solutions with a focus on reliability, performance, and long-term maintainability.
Extensive experience with AWS services, supporting teams across engineering, operations, and leadership. Delivered analytics that drive decisions, reduce risk, and unlock new opportunities. Focused on strengthening infrastructure foundations through thoughtful design and modern engineering practices.
AWS Certifications
Experience
- Spearheaded AWS integration and data analytics initiatives, improving data workflows and enhancing system efficiency.
- Developed and maintained dashboards ensuring accurate data representation for stakeholders, leading to a 90% increase in data-driven decision-making.
- Supported the development of Streamlit applications, boosting user engagement in carbon tracking initiatives.
- Led end-to-end data engineering and integration initiatives, architecting scalable pipelines and analytics solutions across Spotfire, Power BI, and cloud platforms.
- Designed and validated machine learning frameworks including distribution analysis, multi-model testing, and model performance evaluation for accurate forecasting.
- Delivered advanced BI application integrations, developing custom plugins and extensions that improved user adoption across business teams.
- Supervised a team of four data analysts, driving high-quality delivery across SQL development, database management, Spotfire analytics, and enterprise data applications.
- Led midstream reporting operations using Honeywell datasets, ensuring accuracy, consistency, and timely insights for operational and leadership stakeholders.
- Built and maintained enterprise dashboards leveraging Data Lake and multi-source datasets, improving visibility into key metrics across the organization.
- Supported midstream operations through dashboard development and reporting for gas plants and gathering systems.
- Built and maintained analytics reports ensuring data accuracy and operational visibility.
Skills
Cloud & Architecture
- AWS (S3, CloudFront, Lambda, IAM, RDS, Athena)
- Data lake and ETL pipeline design
- Security, access patterns, and IAM
- Infrastructure as Code (Terraform)
Data & Analytics
- SQL, data modeling, performance tuning
- Spotfire, Power BI, dashboard design
- Operational reporting and monitoring
- Data engineering and integration
AI & Automation
- LLM-assisted workflows
- ML framework design and validation
- Automation of repetitive analysis tasks
- Streamlit application development
Leadership & Strategy
- Team supervision and mentoring
- Stakeholder communication
- UX review and analytics adoption
- M.B.A. — Business Administration
Technologies & Tools
Selected Projects
Demo Data Lake for Client Showcases
Designed and implemented an anonymized, date-shifted demo data lake to safely showcase analytics capabilities without exposing sensitive information. Enabled repeatable client demos with production-quality data fidelity.
Operational Dashboards for Midstream Assets
Built performance-focused dashboards for gas plants and gathering systems, balancing usability, speed, and data fidelity. Served operational and leadership stakeholders with real-time visibility into key metrics.
Streamlit Carbon Tracking Application
Supported development of Streamlit applications for carbon tracking initiatives at Crescent Energy, boosting user engagement and enabling data-driven sustainability reporting.
ML Framework Design & Validation
Designed and validated machine learning frameworks including distribution analysis, multi-model testing, and model performance evaluation — enabling more accurate forecasting and data-driven decision support.
UI/UX Review Framework for Analytics Tools
Created a scenario-based, checklist-driven approach to reviewing analytics UIs, improving adoption and reducing user friction across enterprise analytics platforms.
Custom BI Plugins & Extensions
Delivered advanced BI application integrations by developing custom plugins, extensions, and app components that enhanced analytics capabilities and improved user adoption across business teams.
How This Site is Built
This portfolio is deployed on AWS using a fully infrastructure-as-code setup managed with Terraform — practicing what I preach.
Hosting & CDN
- S3 — private static file hosting
- CloudFront — global CDN with HTTPS
- OAC — CloudFront-only S3 access
Security
- ACM — DNS-validated TLS certificate
- TLS 1.2 minimum enforced
- SSL enforced on S3, no HTTP access
- CloudFront access logging enabled
Infrastructure
- Terraform — all infrastructure as code
- Route 53 — DNS and domain management
- S3 versioning and AES256 encryption
- Zero servers — fully serverless
View the full source on GitHub →
Streamlit & AWS
Across my work at Crescent Energy and beyond, I've designed and supported Streamlit applications that turn raw data into interactive tools stakeholders actually use. Here's how I think about building and deploying them on AWS.
Why Streamlit
Fast Time to Value
A Python script becomes a shareable web app in minutes. No frontend overhead means data teams can own the full product — from pipeline to UI.
Built for Data
Native support for dataframes, charts, and metrics means less glue code. I focus on the logic and the story, not the rendering layer.
AWS-Native Fit
Streamlit pairs naturally with S3, Athena, and RDS. Combined with IAM roles and Secrets Manager, it fits cleanly into a secure AWS data architecture.
How I Structure Applications
Clean separation between data, logic, and UI is what makes Streamlit apps maintainable at scale. I consistently apply this layout across projects:
Entry Point
app.py stays thin — routing and layout only. Business logic lives in utils/, reusable components in components/.
Multi-Page Navigation
Each page in pages/ is an independent module. Numbered prefixes control order. This keeps large apps navigable without a custom router.
Secrets & Config
Credentials never touch the codebase. Locally I use .streamlit/secrets.toml. On AWS, environment variables sourced from Secrets Manager.
AWS Deployment Approaches
The right deployment target depends on the audience, scale, and how much infrastructure ownership makes sense for the team.
EC2
Full OS control. I use this when the app needs persistent background processes, custom networking, or sits inside an existing VPC with strict access controls.
ECS Fargate
My go-to for production workloads. Containerized, no server management, integrates cleanly with ALB and IAM task roles. Scales independently of other services.
App Runner
Fastest path from container to HTTPS URL. I reach for this for internal tools and demos where the priority is speed of delivery over infrastructure flexibility.
Key Principles I Apply
- Cache data fetches with
@st.cache_data— S3 and Athena calls should happen once per session, not on every interaction. - Push heavy transforms into the data layer, not the UI layer. Streamlit reruns the full script on every widget interaction.
- IAM roles on the compute layer — never static credentials in code or environment files on the instance.
- Least-privilege access: the app role gets exactly the S3 prefixes, Athena workgroups, or RDS schemas it needs and nothing more.
- Sidebar filters and column layouts keep dashboards scannable. I apply the same UX review framework I use for Spotfire and Power BI.
- Meaningful loading states and error messages — users should always know what the app is doing.
Contact
I'm open to remote roles and collaborations focused on data, cloud, and analytics. Based in Stanley, North Dakota.