Data & Analytics

Most operating companies do not have a data problem. They have a single-source-of-truth problem. Revenue lives in one tool, payments in another, support tickets in a third, marketing performance in a fourth, and the finance team rebuilds the same spreadsheet every Monday morning because nobody trusts the dashboards already there.

Megatrust Technologies fixes that. We build modern data warehouses, clean pipelines, and trustworthy dashboards so the same number means the same thing across every meeting in your company. The work covers the full analytics stack: data ingestion from product databases, payment processors, CRMs, ad platforms, and finance systems, into a properly modelled data warehouse on BigQuery, Snowflake, or managed Postgres.

Transformations live in dbt with version control, tests, documentation, and a clear naming convention. Dashboards are built in Metabase, Looker, Power BI, or Tableau, depending on who is consuming them and what your team already knows. We are deliberate about not picking the trendiest stack.

We pick what your team can operate without us in twelve months. Engagements typically begin with a data source audit. We map every important number in the business: where it lives today, who owns it, how it is calculated, and where definitions diverge between teams.

The audit alone usually exposes the real problem behind your dashboard mistrust: the same metric being calculated five different ways by five different teams. From there we design the warehouse schema, build the ingestion pipelines, write the dbt models that produce business-ready tables, and ship a first set of dashboards that answer the questions leadership has been asking for months. Common projects include executive dashboards that show revenue, retention, gross margin, and pipeline in one place, marketing attribution that links ad spend to customers across paid social, search, and lifecycle email, finance reporting that ties product usage to revenue, operations dashboards for fulfilment and customer support, and product analytics covering funnels, cohorts, retention curves, and feature usage.

When the metric you care about needs prediction rather than measurement, we move into forecasting and anomaly detection, using both statistical models and modern ML. For companies where natural-language access to data matters, we build copilots in partnership with our AI and automation team so leaders can ask questions in plain English and get answers from the warehouse without filing a request ticket. Analytics work depends on solid infrastructure underneath, which is why our data engineers pair with our cloud and DevOps team on hosting, networking, and security, and with our software engineering team on instrumenting the products themselves.

Every engagement ends with a documented platform and a trained team. We train your operators to use the dashboards and your analysts to extend the dbt models, because the goal is to make your data function self-sufficient rather than dependent on us. After handover, retainers cover monitoring, new dashboards, and stakeholder support, but most clients only need a few hours a week once the foundation is in place.

The point is data-confident decisions, made in meetings where everyone is looking at the same numbers, with a shared definition of what those numbers actually mean.