MindLox

Service 07

Data Engineering & Analytics

Pipelines, warehouses, and dashboards that produce one trusted number instead of four plausible ones - with tests on the data itself.
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App DBProvider APIFiles / EDIRawappend-onlyModelleddbt, in gitMetriclayerdefinedonceData quality assertionsfreshness · volume · uniqueness · rangesfail = alert,not a wrong chart

The problem

Three dashboards, three different numbers, one meeting lost to arguing

The usual symptom is not missing data - it is competing data. Each team defines revenue, active user, or churn slightly differently, each definition lives in a separate query, and nobody can reconcile them quickly enough to make the decision at hand.

Meanwhile reporting consumes real time. Someone exports, cleans, and pivots the same spreadsheet every Monday, and when they are on holiday the report simply does not happen.

Our approach

Define the metric once, test it, then automate the reporting

We start with a metric layer: the handful of numbers the business actually runs on, each with a written definition agreed by the people who use it. Without that agreement, better infrastructure just produces faster disagreement.

Pipelines are built to be re-runnable and observable. Transformations live in version control, run on a schedule with alerting, and are safe to replay when an upstream source corrects history - which it will.

We test the data, not just the code. Freshness, row-count variance, uniqueness, referential integrity, and acceptable ranges are asserted on every run, so a broken upstream feed raises an alert instead of quietly producing a confident, wrong chart.

Capabilities

What this service covers

Individual capabilities are scoped per engagement - this is the range we work across, not a list every project includes.
  • Data pipeline development
  • ETL and ELT systems
  • Data warehouse implementation
  • Business intelligence dashboards
  • Analytics and event tracking integrations
  • Data quality testing and validation
  • Reporting automation
  • Predictive modeling and forecasting

Deliverables

What you actually receive

Illustrative artifacts from a typical engagement. The exact set is agreed during discovery and written into the statement of work.
  1. 01

    Metric definitions

    A written, agreed definition for each core business metric, with its source and calculation documented.

  2. 02

    Pipelines in version control

    Scheduled, re-runnable transformations with lineage, so you can trace any number back to its source.

  3. 03

    Warehouse model

    A modelled layer designed for the questions the business asks, rather than a mirror of application tables.

  4. 04

    Data quality suite

    Automated assertions on freshness, volume, uniqueness, and ranges, with alerting on failure.

  5. 05

    Dashboards people use

    A small number of decision-oriented views, built with the people who will act on them.

Engagement

How a project in this area runs

Tailored to this service rather than a generic four-box process diagram.
  1. 01

    Decision mapping

    Which decisions need data, which metrics inform them, and where those metrics disagree today.

  2. 02

    Pipeline and model

    Ingestion, a modelled warehouse layer, and the metric definitions implemented once and reused.

  3. 03

    Quality and automation

    Data tests, scheduling, alerting, and replacement of the manual reporting that eats someone's Monday.

  4. 04

    Dashboards and enablement

    Decision-oriented views plus training, so analysts extend the model instead of forking it.

Technology

Options we support for this work

Chosen per project against your team's existing skills and what you will maintain afterward - not a requirement list.

Warehouse

  • PostgreSQL
  • BigQuery
  • Snowflake
  • DuckDB
  • ClickHouse

Transformation

  • dbt
  • Python
  • SQL
  • Dagster
  • Airflow

Ingestion

  • Airbyte
  • Fivetran
  • Change data capture
  • Custom connectors

Analytics

  • Metabase
  • Looker Studio
  • Superset
  • Embedded dashboards

Questions

What clients ask about Data Engineering & Analytics

Do we need a warehouse, or is our database enough?

If reporting queries are not straining your production database and the questions are simple, your database is enough - and we will say so. A warehouse earns its place when analytical queries start competing with the application, when you need to combine several sources, or when you need history your application deliberately overwrites.

Can you work with the tools we already pay for?

Yes, and usually we should. Most organizations have more capability in their existing stack than they use. Replacing tools is a last resort, not a starting assumption.

What about predictive models?

Worth doing once the foundations hold. A forecast built on untested pipelines inherits every data quality problem and hides it behind a confident number. We sequence it after the metric layer and data tests are in place.

How do you handle sensitive data?

Minimise, segregate, and control access. We avoid copying personal or regulated data into analytical systems unless a specific question requires it, apply masking or aggregation where it does, and document retention. Where a regulatory framework applies, we work to your compliance team's requirements rather than asserting our own certification.

Related

Related work and services

Ready to talk about Data Engineering & Analytics?

Tell us what you are trying to achieve. If we are not the right fit, we will say so and point you somewhere better.

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