Data visualization services — engineers who build dashboards that don’t lie
Most dashboard problems are data problems. The charts are wrong because the underlying model is wrong: undefined metrics, inconsistent joins, no single source of truth. The engineers on this platform fix the data layer first, then build the visualization on top of something solid.
What these engineers build
BI dashboards
Tableau, Looker, Power BI, Metabase — the engineers work with all major platforms. Tool choice usually comes down to what your team already has licenses for and what your downstream users know. If you’re choosing fresh, they’ll tell you which fits your query patterns and user type.
Semantic layers
A semantic layer (LookML in Looker, a dbt metrics layer, or a Tableau data source with defined calculations) means business users query the same definitions every time. Revenue means one thing, not seven different things depending on who wrote the query. The engineers build the semantic layer as part of the engagement, not as an afterthought.
Data marts purpose-built for reporting
Reporting queries that hit raw transactional tables are slow and fragile. The engineers build purpose-built data marts — pre-aggregated, denormalized tables designed for dashboard query patterns. Your dashboard loads in 2 seconds instead of 45.
Real-time dashboards
For use cases that need live data (operations monitoring, support queue tracking, live sales performance), the engineers build streaming pipelines feeding low-latency data stores. Kafka, Kinesis, or Change Data Capture from the source database, depending on your infrastructure.
The underlying problem most teams don’t fix
A dashboard is only as trustworthy as the data model underneath it. The most common reason dashboards get ignored is that the numbers don’t match: two people pull “revenue for last quarter” and get different answers. No one trusts either one.
The engineers on this platform are data engineers first, visualization developers second. They’re hired to fix the cause, not paper over it. That means:
- Defining metrics precisely before building a single chart
- Writing the dbt models (or equivalent) that produce consistent, tested output
- Documenting what each metric includes and excludes, so the “which revenue?” question has a single answer
- Then building the visualization layer on top of data that’s actually reliable
If you bring them an existing Tableau workbook that produces wrong numbers, they’ll identify where the logic breaks and fix it at the source — not with calculated fields in the viz tool.
Typical team
1 data engineer + 1 BI analyst/developer
The data engineer owns the data model and pipeline: source system connections, staging transforms, mart tables, semantic layer definitions. The BI developer owns the visualization layer: dashboard design, calculated fields, user access configuration, performance optimization.
These two tracks need to run in parallel. The BI developer can’t build reliable charts without a clean data model. The data engineer can’t know what the mart needs to contain without knowing what the dashboards will ask of it. They work together from the start.
For smaller scopes (one source system, a few dashboards), a single senior BI engineer who’s strong in both tracks can handle it. For anything more than that, the two-person team is faster and produces better output.
Tech stack
| Area | Technologies |
|---|---|
| BI tools | Tableau, Looker, Power BI, Metabase |
| Semantic layer | dbt metrics layer, LookML, Tableau data sources |
| Data warehouse | Snowflake, BigQuery, Redshift, Databricks |
| Transformation | dbt Core, dbt Cloud |
| Languages | SQL, Python |
| Streaming (real-time) | Kafka, AWS Kinesis, Debezium (CDC) |
How billing works
You rent the engineers. You own the dashboards, data models, dbt code, and documentation produced during the engagement. staffai.eu employs the engineers — you direct the work.
Billing is hourly, settled monthly. A typical engagement runs 2–4 months for an initial build: data model design, mart construction, dashboard build, user training. After that, most clients keep one engineer on at reduced hours for ongoing maintenance and new dashboard requests.
Related pages
Get a team estimate
Tell us what you’re building — the tools, the source systems, the number of dashboards — and we’ll come back with a team configuration and monthly cost.