Service · Remote-first senior specialists
Reporting people act on starts with an agreed definition of every number on it. MV.tech builds semantic and dimensional models, curated reporting layers and dashboards in Power BI, Tableau, Looker Studio and Streamlit — and reviews the models you already have before proposing that anything is rebuilt.
The starting point
What we deliver
Fact and dimension tables at an agreed grain, with conformed dimensions, bridge tables for many-to-many relationships and source-load tracking. The model carries the business logic, so each report reads the same definitions instead of re-implementing them.
Datasets, relationships, calculation groups and measures in DAX, with row-level security where the data needs it. Existing models are profiled first — storage mode, cardinality, unused columns, duplicated measures — so a rebuild becomes a justified decision rather than a default.
Document what a tabular model actually contains: tables, relationships, calculated columns, measures and the reports depending on each one. The output is a written assessment of what to keep, what belongs in the warehouse instead and what can be retired.
Dashboards built on curated views rather than ad-hoc extracts, published to Tableau Server, Looker Studio, or a Streamlit and Plotly application where an interactive Python tool fits the job better than a BI licence.
Write each metric down: the calculation, the filters, the grain, the owner and where it is allowed to appear. Past engagements have defined 20+ governed KPIs this way and cut reporting inconsistencies by around 40%, mostly by removing definitions that had quietly diverged.
Reduce what a report has to read: partitioning, incremental refresh, aggregation tables, pushing transformation back into the warehouse and removing columns nothing uses. On past work this has reduced query scan sizes by 60–75% and moved refreshes from hours to minutes.
A curated dataset analysts can slice without writing joins: naming standards, hidden technical columns, a description on every field and a clear line between governed measures and someone’s own analysis. Handover includes the measure catalogue, refresh schedule, access model and how to extend it.
A useful first project
Take the report a team or an executive actually uses and rebuild it properly. Agree the grain, write down the definition of every number on it, model the facts and dimensions behind it, and reconcile the result against the figures people trust today. That gives the semantic model a spine other reports can hang off, and it gives the project an acceptance test that is not a matter of opinion. Further dashboards, self-service datasets and a wider semantic layer are scoped once the first one is agreed.
See our delivery processWorking together
Inventory reports, models and measures, profile refresh times and query patterns, and record which numbers disagree and why.
Build the semantic model at an agreed grain and write the metric definitions alongside it, reconciling each measure against a trusted source.
Release with access rules, refresh monitoring and documentation, then work with the people who will extend it.
Teams our engineers have worked with
Before we begin
Not answered here? Ask us directly or read the full FAQ.
Usually yes, and that is where we prefer to start. A review covers grain, relationships, calculated columns, duplicated measures, storage mode and refresh behaviour, and produces a written list of what to fix in place and what genuinely needs rebuilding. A rebuild is something we have to justify, not a starting assumption.
It is the layer between your warehouse and your dashboards that holds tables, relationships and agreed measures. When it exists, every report calculates revenue the same way. When it does not, each dashboard re-implements the logic and the numbers drift apart. It also lets analysts build their own views without writing joins.
Almost always upstream of the dashboard. The usual causes are different filters, a different grain, different date logic or a metric defined twice. We trace each figure back to its source, agree one definition per metric with a named owner, and move the calculation into the model so a report is no longer the place the logic lives.
Whichever one your team will maintain. Power BI suits organisations already in Microsoft 365 that need a governed semantic layer; Tableau suits heavy visual exploration; Looker Studio is reasonable for lighter reporting over Google Cloud data; Streamlit fits when the output is really an interactive Python tool. We would rather improve the tool you have than sell you a migration.
Not always, but usually. A semantic model can read an operational database for a while and sometimes that is the right call. Once reports need history, several sources, or a grain the source system does not store, a curated warehouse layer costs less than repeatedly patching reports. We can tell you which situation you are in after looking at the sources.
Related services
ETL/ELT pipelines, BigQuery, Snowflake and Redshift warehouses, migrations, semantic models and reporting your team can trust.
ExploreDedicated offshore data engineering and analytics capacity from India for ETL, cloud warehouses, BI, migration and data quality work.
ExploreKnowledge assistants grounded in your documents, MCP servers and custom connectors exposing approved tools, and LLM features with evaluation built in.
ExploreYour next step
Bring your goal, current tools and preferred working hours. We use the first 30-minute conversation to clarify fit and an initial scope, and you leave with a written next step.
Remote from Ahmedabad, India · Overlap with any timezone · No obligation
