Career Portfolios for BI Developers
A BI portfolio can make semantic models, DAX, ETL decisions, and dashboard interaction understandable without publishing sensitive source data or internal systems.
Model reasoning
Explain the grain, facts, dimensions, star or snowflake choices, relationships, and measures that shaped a semantic model.
Governed delivery
Make room for Power Query, M, data quality, row-level security, deployment, documentation, performance, and ownership decisions.
Technical evidence with boundaries
Show enough architecture and interaction to demonstrate depth while keeping enterprise data, connections, and access details private.
How to present BI architecture and delivery.
Show the model grain, facts and dimensions, and why a star or snowflake structure was chosen. Connect semantic models, DAX, calculation groups where relevant, Power Query, M, and ETL to data quality, row-level security, governance, deployment, performance, and documentation decisions.
- A positioning layer for the BI development work you want to be hired to do.
- Case-study pages that connect semantic-model design to the questions a report needed to answer.
- Technical sections for DAX measures, calculation groups, Power Query, M, ETL, security, and governance.
- Clear treatment of synthetic demonstrations, redacted screenshots, and limitations.
- Navigation that lets a recruiter scan while preserving a path into the technical work.
Workforce-events semantic model
This fictional planning example is not client work and was not built from an employer system. It outlines a privacy-safe BI case study using synthetic workforce-event records only.
Fact-table grain
One synthetic workforce event per row, keyed to a date, department, manager role, and status; no named person or employee record is included.
Semantic-model design
A star-shaped model separates descriptive dimensions from Fact Workforce Events, with relationships and measure ownership documented in the model.
Power Query and ETL
An illustrative staging flow standardizes status values, validates keys, rejects duplicate event identifiers, and records transformation decisions in Power Query or M documentation.
Row-level security
A conceptual department-access mapping demonstrates how authorization could filter the model; it contains no real identities, access assignments, or security configuration.
Data-quality rule
Every event must have one valid event date, status, and department key before entering the reporting fact table; exceptions remain visible in a quality log.
Deployment and governance
Describe development, review, and production stages; ownership; refresh expectations; measure review; and change-control responsibilities without claiming a real deployment.
Documentation
Include grain, relationship diagram, transformation notes, DAX definitions, calculation-group rationale where relevant, security concept, quality rules, performance checks, and limitations.
Synthetic-data disclosure
All model elements and values are fictional. No real employer, workforce, client, deployment, or measured outcome is represented.
- Dim Date
- Dim Department
- Dim Manager
- Dim Status
Each dimension filters the event-grain fact table. The diagram communicates model intent without exposing any real organization, identity, source, or access design.
Event Count =
COUNTROWS(
'Fact Workforce Events'
)The base measure makes filter behavior explicit and can support documented derived measures. Any calculation group or downstream rate would still need a stated business definition.