Analytics & Business Intelligence
I build Power BI and SQL reporting around operational questions, reporting logic, and SLA performance.
Anstead Analytics Engineering.
Analytics Engineer /
Data Solutions Engineer
Analytics engineering / Data solutions / Business intelligence / Systems thinking
01 / OBSERVEThe system
02 / MODELThe relationships
03 / BUILDThe solution
04 / VALIDATEThe decision
01 / ENGINEERING APPROACH
Understand the system.
Then engineer the solution.
My approach to analytics developed across technical disciplines, not from reporting alone.
My background connects electronics engineering technology, troubleshooting, information systems, and operations management with programming, databases, business intelligence, and data engineering. I bring that combination to the way I analyze technical problems.
When I encounter an unfamiliar system, I decompose it into components, trace inputs and outputs, and map relationships, interfaces, and data flows. I look for boundaries, constraints, and failure modes before reconstructing the logic of the larger system.
The system behind the dashboard
A dashboard is not the system. It is one interface into a larger analytical system. Source systems, business rules, pipelines, transformations, databases, semantic models, and security shape what it can show. Operational processes, users, decisions, and feedback loops determine whether it serves its purpose.
- 01Observe
Understand the system, users, data, and actual problem.
- 02Decompose
Break the system into understandable components.
- 03Map
Trace relationships, dependencies, flows, interfaces, and constraints.
- 04Model
Represent the system in a form that can be analyzed.
- 05Build
Implement the appropriate technical solution.
- 06Validate
Check the model and its support for the intended decision or process.
- 07Improve
Refine the system, model, or process using what was learned.
02 / CAPABILITIES
Data, software, and operations.
Connected through engineering.
Professional focus: analytics engineering, BI engineering, data engineering, and data architecture.
Data Engineering & Databases
I work with relational databases, SQL transformations, and data integration between operational and reporting systems.
Semantic Modeling
I connect business rules, model relationships, and DAX calculations so analytical logic represents the underlying process.
Programming & Automation
I use programming and APIs to automate workflows, exchange data, and connect applications that do not work together directly.
Systems & Architecture
I reverse engineer application behavior, map dependencies and interfaces, and troubleshoot how systems exchange information.
Operations & Delivery
I connect technical changes to operational workflows, process constraints, and the people responsible for delivery.
03 / FEATURED WORK
The reasoning.
Not just the screenshot.
One evidence-backed case study, a working lab template, and clearly marked future concepts.
CASE STUDY / EVIDENCE-BACKED
Brownfield engineering / real case study
Diagnose, Adapt & Enhance
Recovering reliable browser validation in an existing application through observable test points, controlled input, and retained evidence.
Explore Case Study
CONCEPT / PLACEHOLDER
Architecture / future case study
Enterprise Analytics Architecture
A future examination of source dependencies, analytical layers, and the decisions behind an enterprise design.
CONCEPT / PLACEHOLDER
Business intelligence / template lab concept
Power BI: Default vs Engineered
A planned comparison of standard reporting patterns and deliberately engineered experiences.
Explore Template Case Study
CONCEPT / PLACEHOLDER
Data engineering / future case study
Data Pipeline Architecture
A future walkthrough of ingestion, transformation, quality boundaries, and analytical delivery.
CONCEPT / PLACEHOLDER
Security / future case study
Analytics Security by Design
A planned investigation of identity, access boundaries, and security within an analytics architecture.
CONCEPT / PLACEHOLDER
Systems analysis / future case study
Product & Domain Knowledge
A future example of reverse engineering a domain and translating its constraints into a technical model.
04 / ANALYTICS LAB
Power BI:
Default vs Engineered.
The same analytical question.
Two approaches to the experience.
Future demonstrations
STANDARD APPROACH
Default report interactions
A future demonstration of the report's built-in interaction behavior.
Demonstration pendingENGINEERED APPROACH
Intentional interaction design
A future demonstration of interactions designed around analytical intent.
Demonstration pendingExisting public Power BI demo UDATE / STANDALONE REPORT
This existing demo is separate from the future Default vs Engineered comparison.
Open public report05 / ARCHITECTURE & PIPELINES
Follow the data.
Understand every dependency.
Vendor-neutral reference concept.
A real implementation and its constraints will follow.
- 01
Source systems
Operational context
- 02
Ingestion
Capture & transport
- 03
Transformation
Rules & quality
- 04
Data warehouse
History & structure
- 05
Semantic model
Meaning & context
- 06
Analytics
Explore & explain
- 07
Business decision
Action & feedback
Validate assumptions. Improve the system.
06 / KNOWLEDGE & COMMUNICATION
Engineering worth explaining.
Future presentations, walkthroughs, tutorials, and downloadable artifacts.
CONCEPT / PLACEHOLDER
Presentation / future artifact
Technical Presentation
A future technical deck connecting a problem, its architecture, and the engineering decisions.
CONCEPT / PLACEHOLDER
Video / future artifact
Video Walkthrough
A future narrated walkthrough of a system, implementation, and validation evidence.
CONCEPT / PLACEHOLDER
Tutorial / future artifact
Engineering Tutorial
A future reproducible explanation with examples, code, and checks.
CONCEPT / PLACEHOLDER
Architecture / future artifact
Architecture Deep Dive
A future exploration of dependencies, design alternatives, and trade-offs.
07 / EDUCATION & CREDENTIALS
An interdisciplinary foundation.
Bachelor of Interdisciplinary Studies (B.I.S.)
Applied Management Information Systems and Operations Management
University of North Carolina at Pembroke
Graduated: 2020
This interdisciplinary foundation combines information systems, technology, business decision-making, and operations. It informs how I connect data and software with the processes and decisions they support.
Highlights of the program included:
- Core courses in programming, system administration, and database management
- Operations Management and Project Management
- Applied Business Statistics
- Courses in Technology and Society, and Conflict Management
Associate in Applied Science (A.A.S.)
Electronics Engineering Technology
Gaston College
Graduated: 2010
This two-year program, accredited by ABET, provided a foundation in electronics, circuits, digital systems, and technical troubleshooting. It informs how I reason about components, hardware/software interfaces, and engineering problems.
- Design and troubleshooting of electronic systems
- Programming in C and C++
- Hands-on experience with analog and digital electronics
- Courses in mathematics, physics, and statistics
Awards:
- President's Award for Excellence, May 4, 2010
- Most Outstanding Graduate in Computer Engineering Technologies, Industrial and Engineering Technologies Division
CREDENTIAL FOCUS
Relevance to analytics engineering.
Cell area reflects my own assessment (1 to 10) of how directly each credential supports analytics engineering. Colors follow the portfolio tiers below. This is a portfolio view, not an issuer ranking.
- Foundation
- Professional
- Expert
- Specialist
Portfolio visual tiers, not official Microsoft or CompTIA rankings.
POWER BI / TECHNICAL PROJECTS
Featured Demonstrations
Three complementary scopes of Power BI practice, from foundational reporting to analytics engineering.
Build clear, dependable reports from imported data and straightforward business questions.
- Data import and basic Power Query transformations
- Basic DAX aggregations
- Standard charts and tables
- Slicers and filters
- Simple relationships
Shape trusted business models and polished dashboards around the decisions teams need to make.
- Star-schema modeling
- KPI and time-intelligence measures
- Drillthrough and report tooltips
- Field parameters
- Professional navigation and formatting
- Business-focused dashboards
Engineer governed analytical products through reusable semantic models, advanced calculations, and automated validation.
- Centralized semantic models
- Advanced DAX and reusable calculations
- Dynamic temporal analysis
- Composite models and DirectQuery
- Security and governance
- Performance optimization
- Automated development and validation
Filter projects by category
Power BI Analytics / Data Engineering / Security & Governance
Engineered Analytics Workspace
An engineered analytics workspace demonstrating connected reporting, semantic modeling, and decision-focused views.
DAX Explorer
DAX Explorer
A searchable catalog of DAX functions and measures, with practical examples and clear explanations of how calculations behave.
DEMONSTRATION / PLANNED
Searchable function & measure catalog
Report preview will appear here when published.
Power BI Analytics
Foundational Power BI Dashboard
A focused introductory report covering imported data, basic transformations, simple relationships, core aggregations, and interactive filters.
DEMONSTRATION / PLANNED
Core charts, tables & report filters
Report preview will appear here when published.
Power BI Analytics
Professional BI Dashboard
A business-ready dashboard built around a star schema, KPI and time-intelligence measures, guided navigation, and contextual analysis.
DEMONSTRATION / PLANNED
Business model & decision-focused dashboard
Report preview will appear here when published.
No projects in this category at this level yet.
SCOPE / RESPONSIBILITY
How the levels build
Level 1
Foundational Reporting
- Focus
- Answer a focused reporting question
- Data foundation
- Imported data and simple relationships
- Responsibility
- Core report building and exploration
Level 2
Professional Business Intelligence
- Focus
- Support recurring business decisions
- Data foundation
- Star schema and reusable KPI measures
- Responsibility
- Navigation, analysis, and stakeholder-ready delivery
Level 3
Analytics Engineering
- Focus
- Deliver governed, scalable analytical products
- Data foundation
- Central semantic models and composite architectures
- Responsibility
- Security, performance, automation, and validation
PROFESSIONAL BACKGROUND
The experience behind the approach.
Existing professional history
08 / LET'S CONNECT
Discuss an analytics
or data systems problem.
Analytics engineering, business intelligence, data integration, and automation.