Assignment: Build Two Dashboards
This page answers two questions students ask every week: why am I learning this, and how does it help me find a job?
It is scoped to Week 11 content (dashboarding with Metabase and Streamlit). Other weeks' career pages each cover their week's tool. Generic NL junior data-career content (salary bands, day-to-day work, what employers do not expect from juniors) lives in one shared page across the curriculum and is not repeated here.
The numbers below are a rough reading of public NL postings as of June 2025. They are indicative, not measured. A separate project crawls Dutch data postings and will replace the qualitative claims here with measured percentages once the dataset is ready; placeholders are marked ~XX% for that swap.
| Tool | Data analyst | Analytics engineer | Data engineer | Data scientist |
|---|---|---|---|---|
| Power BI | Very common (~70% of analyst postings) | Common | Rare | Occasional |
| Tableau | Common (~40%) | Occasional | Rare | Occasional |
| Looker / Looker Studio | Common (~30%, especially scale-ups) | Common | Occasional | Rare |
| Metabase | Occasional (~15%, often start-ups) | Occasional | Occasional | Rare |
| Streamlit / Python data apps | Rare | Occasional | Common (~30%) | Common (~35%, model demos) |
What this means in practice: If you target a data analyst role in NL, Power BI is the dominant tool to know. It dominates largely because it is effectively "free": it ships bundled with the Microsoft 365 / Fabric licensing most NL companies already pay for, so adopting it costs nothing extra. Metabase is a good learning vehicle: it teaches the same mental model (Questions, Dashboards, filters) without a Microsoft licensing cost, and without Power BI's heavier semantic layer and M / DAX languages on top. Most NL employers who use BI will use Power BI or Tableau in production. Streamlit is more relevant for data engineering roles where custom monitoring apps are expected.
NL postings rarely require Metabase by name. What they require is "experience with a BI tool." Metabase is an effective way to demonstrate that experience because the underlying skills (SQL, chart design, dashboard layout, metric definition) transfer directly to Power BI, Tableau, or Looker.
The practical implication: on your CV, write "Metabase (transferable to Power BI/Tableau)" rather than listing Metabase alone. Recruiters screening for Power BI experience will skip a CV that only mentions Metabase.
For roles that explicitly list Metabase (early-stage start-ups, companies with open-source-first stacks), the Week 11 assignment is directly applicable.
Junior postings typically say:
Medior postings typically say:
The Week 11 assignment clears the junior bar: you connect a BI tool to a production database, write SQL, and document metrics. The gap to medior is operational experience: maintaining dashboards over time, handling stakeholder requests, managing metric drift.
Strong CV line:
"Built an analytical dashboard in Metabase on top of a dbt-modelled PostgreSQL database (NYC taxi data), documenting metric definitions for each panel. Built a companion Streamlit metrics app on the same marts, with KPI tiles, a trend chart, and a data-freshness panel."
Recruiter keywords carried: Metabase, dbt, PostgreSQL, Streamlit, SQL, metric documentation, data visualisation.
Weaker alternative (avoid):
"Created dashboards and visualisations using Metabase and Streamlit."
The weaker version lists tools without context. The strong version tells a story: you modelled data, surfaced it through a BI tool, and built a code-first metrics layer on the same marts. That is a complete data product, not just a chart.
When an interviewer asks "tell me about a data project you have built":
"I built the visualisation layer for a pipeline that ingests NYC taxi data and transforms it with dbt. I created an analytical Metabase dashboard with three panels (trip volume by borough, daily revenue trend, and average tip rate) and documented metric definitions for each. I also built a Streamlit metrics app on the same marts, with KPI tiles, a daily-trend chart, and a data-freshness panel. The two tools reflect the split between point-and-click BI for stakeholders and code-first dashboards when you need custom logic."
Two honest "what would you do differently?" follow-ups:
<aside> 💭 For generic NL junior data-career content (salary bands, day-to-day work, what employers do not expect from any junior), one shared page across all weeks is the right home. That page does not exist yet; for now, treat this page as Week-11-specific only.
</aside>
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