Characters & Roles

YellowLine NYC engagement — MHP consulting team

Use these names consistently in modules, exercises, and discussion.

Character Role Narrative function
Marcus Chen Operations Manager, YellowLine NYC Client sponsor; states business pain and constraints
Elena Vasquez Data Architect, MHP Designs medallion architecture; leads tool evaluation
Bob Muller Junior Data Engineer, MHP Hands-on builder; trainee proxy
Sofia Alvarez Senior Data Engineer, MHP Mentors Bob on Databricks / PySpark
Priya Sharma BI Analyst, MHP Defines KPI questions; builds Power BI dashboard in the story
James Okonkwo Data Analyst, MHP Validates KPI logic with SQL

Character Details

Marcus Chen — Operations Manager at YellowLine NYC. Runs a fleet of 400+ yellow taxis across all five boroughs (Manhattan-heavy volume). Frustrated by spreadsheets that break and dashboards that lag behind reality. His three non-negotiable constraints: cost (TCO must be justifiable to the board), performance (dispatch needs near-real-time visibility), and compliance (audit-proof numbers for Q3 regulatory review). He doesn’t care about technology — he cares about outcomes.

Elena Vasquez — Data Architect at MHP. 12 years of experience across banking, logistics, and mobility. Designed the medallion architecture that all three pipelines follow. Vendor-neutral by principle — she evaluates tools on merit, not marketing. Her closing line in Module 7: “Technology is a decision. Architecture is responsibility.” She approves all tool pivots; Bob never “picks vendors” alone.

Bob Muller — Junior Data Engineer at MHP, 2 years out of university. The trainee proxy — when trainees build pipelines, they are Bob. He asks the questions trainees are thinking but might not voice. Elena mentors him through architecture decisions; Sofia guides him through Databricks specifics.

Sofia Alvarez — Senior Data Engineer at MHP. Databricks specialist who guides Bob through PySpark, Delta Lake, and cluster management in the Module 2 lab. Snowflake rebuild is Elena + Bob — Sofia does not own the Snowflake port. Also supports optional streaming and ML stretch labs.

Priya Sharma — BI Analyst at MHP. Captures Marcus’s five KPI questions; Bob materialises twelve Gold kpi_* tables so each question has the right grain. Builds the five-page Power BI dashboard in the story. Her Gold schema is the contract — same tables, same columns, regardless of which pipeline engine produced them.

James Okonkwo — Data Analyst at MHP. Validates KPI logic by writing independent SQL checks against Gold tables — especially kpi_data_quality_metrics in Module 4. Ensures the numbers Priya’s dashboard shows match the source data.

Think & Discuss

Situation (Story day — before Module 1): YellowLine NYC has millions of taxi trips but no analytics platform. Marcus hired MHP. Priya needs KPIs; Elena wants medallion architecture — nothing is built yet. Sketch your initial design; Module 1 formalizes layer semantics. Save this whiteboard — revisit in Module 7.

Prompts:

  1. What is Marcus’s biggest problem in your own words?
  2. Where does trip data live today? How often should it be refreshed?
  3. If you split data into layers, how many would you use and what goes in each?
  4. Name two tools you would consider for the pipeline. Why those two?
  5. Priya needs a dashboard — what must exist in the pipeline before she can build it?
  6. What could go wrong (data quality, cost, skills, maintenance)?

Do not reveal answers yet — Module 1 and labs validate your ideas.

Trainee instruction

Today you are Bob. Elena designed the architecture; your job is to build, evaluate, and recommend tools for Marcus.

Role boundaries (credibility)

  • Elena approves tool pivots (Databricks → Snowflake → dbt) — Bob does not “pick vendors” alone.
  • Sofia mentors Databricks/PySpark — she does not replace Elena’s architecture sign-off or James’s SQL validation.
  • James independently checks Gold KPIs in SQL — he does not redefine Priya’s dashboard layout.
  • dbt is always framed as a transform layer on Snowflake, not a warehouse replacement.
  • Priya consumes Gold KPI tables in Power BI — same schema regardless of pipeline engine.