Dashboards where every number has one definition.
Power BI engineers who build semantic models on tested marts: row-level security, deployment pipelines, refresh targets agreed with you and DAX reviewed like code, so teams can stop arguing over whose figure is right.
- Power BI
- Power BI Embedded
- DAX
- Tabular Editor
- DAX Studio
- dbt
The reporting layer many enterprises already use.
Power BI is the reporting layer many enterprises already use — it sits inside the Microsoft estate they already run, the finance team knows it, and the security team has already approved it. That makes it the pragmatic choice for the last mile of a data platform: the place where a governed model becomes a page a director opens on Monday morning. We use it because it is where the decisions get read, and because a well-built semantic model in Power BI is a genuinely good piece of engineering — one definition of revenue, margin and churn, reused by every report.
The reason a Power BI estate disappoints is usually not the tool. It is a hundred workbooks that each import a CSV, each define margin a little differently, and each refresh when someone remembers. We build it the other way round: transformations live in dbt models that are tested and documented, a small number of certified semantic models sit on top, and reports are thin. Analysts still build what they need — on a model whose numbers agree with the finance system.
We do not use Power BI as a data-integration tool, an operational application or a substitute for a warehouse. Power Query is for light shaping, not for the business logic; that belongs in the pipeline where it can be tested.
Six things we ship with Power BI.
Certified semantic models
Star-schema models on top of warehouse marts, with conformed dimensions, documented measures and a single owner — the model analysts connect to instead of the raw tables.
Executive and operational dashboards
Finance, sales, operations and loyalty-programme reporting designed around the questions people actually ask, with drill paths to the transaction and a refresh time on the page.
Embedded analytics
Power BI Embedded inside your own products and portals, with app-owns-data authentication, row-level security driven by the calling user and capacity sized to real usage.
Row-level and object-level security
Security roles built from the same identity and hierarchy data the business uses — region, business unit, customer — tested with impersonation before anyone outside the project sees a number.
Refresh and performance engineering
Incremental refresh, aggregation tables, composite models and DirectQuery only where freshness demands it — with refresh targets agreed with you and monitored, not assumed.
Governance and lifecycle
Workspaces by domain, deployment pipelines from development to production, endorsement rules, usage metrics and a retirement process for reports nobody opens.
When to choose it.
Choose Power BI when your organisation runs on Microsoft 365 and Azure, when the audience is business users who want to explore a certified model rather than wait for a report request, and when you need row-level security, scheduled refresh and enterprise sharing without building them. Choose it for the reporting layer of a data platform built on Azure, Snowflake, BigQuery or a well-modelled PostgreSQL warehouse. Choose Embedded when analytics belong inside a product you sell or a portal you run.
Do not choose it as the place where business logic lives — that belongs in tested dbt models, not in a chain of Power Query steps nobody can review. Do not choose DirectQuery over a slow source and expect a fast page. Do not choose it for real-time operational monitoring at second-level latency, where Grafana or a purpose-built screen serves better. And if your estate is Google-first and the team already lives in Looker, we will say so rather than move you.
Measures with one definition, reviewed in source control.
Net Revenue =
CALCULATE ( SUM ( fct_sales[net_amount] ), fct_sales[status] = "invoiced" )
Net Revenue LY =
CALCULATE ( [Net Revenue], SAMEPERIODLASTYEAR ( dim_date[date] ) )
// margin uses the same cost basis as the finance ledger — see dbt model fct_sales
Gross Margin % =
DIVIDE ( [Net Revenue] - SUM ( fct_sales[cogs_amount] ), [Net Revenue] )
+ Active Members = DISTINCTCOUNT ( fct_loyalty_activity[member_id] )From source system to a page a director trusts.
What the Power BI estates we hand over look like.
- Logic upstream, models thin. Business rules live in dbt where they are tested and documented; Power Query does light shaping only. If a definition needs to change, it changes once.
- Star schemas by default. Fact and dimension tables with conformed dates, customers and products. No wide flat tables, no bidirectional relationships used as a shortcut.
- Measures reviewed like code. DAX kept in source control with Tabular Editor, named and documented consistently, reviewed in pull requests and profiled with DAX Studio before release.
- Security tested, not assumed. Row-level and object-level roles derived from the identity source, verified by impersonation in a test plan, and reviewed whenever the organisation chart changes.
- Refresh targets are agreed with you. Incremental refresh on large facts, refresh history monitored, failures alerted to an owner, and the last-refresh time shown on every page.
- Performance budgets on pages. Visual counts kept sane, aggregations where the model is large, and a page-load target checked with Performance Analyzer before sign-off.
- Lifecycle through pipelines. Development, test and production workspaces with deployment pipelines, endorsed and certified datasets, and no report promoted by copying a file.
- Personal data minimised. Only the attributes a report needs reach the model; sensitive columns are masked or excluded, sensitivity labels applied, and KVKK and GDPR access rules reflected in security roles.
What usually ships alongside it.
- Certified semantic models with a measure dictionary the business has signed off
- dbt marts with tests and lineage feeding the models, so the number in the report traces to its source
- Row-level security roles with a documented test plan and impersonation results
- Deployment pipelines across development, test and production workspaces, with a release checklist
- Refresh and usage monitoring with alerting, and a regular retirement list for reports nobody opens, at a rhythm agreed with you
Frequently asked questions
Neighbouring technologies and the service that uses them.
Need Power BI engineers who model before they chart?
Tell us which numbers your teams disagree about and what your warehouse looks like. We will propose a named team or place analytics engineers into yours.