Data engineering & analytics
Pipelines that don't silently break, a warehouse that reconciles, and dashboards where the number matches what finance says. The unglamorous work that makes reporting worth having.
Data work is three separate jobs that get bundled into one word. Data engineering moves information reliably from the systems that create it into somewhere it can be queried. Data modelling decides what a customer, an order or revenue actually means, once, so everyone counts the same way. Analytics turns that into something a person can act on.
Most companies that feel they have a reporting problem actually have a modelling problem. Two teams report different revenue numbers not because a dashboard is broken but because nobody ever decided whether revenue includes tax, cancelled orders or intercompany transfers. No amount of charting fixes that.
The typical mid-market starting point is a set of exports into spreadsheets, reconciled by hand each month by someone whose time is expensive. That's the problem worth solving first.
Four stages. Knowing which one you're at determines what's worth doing next — and skipping a stage rarely works.
- 01
Spreadsheet reporting
Monthly exports, manual joins, one person who knows how it works, numbers that don't always agree.
Next stepAutomate the extract. Even landing raw data somewhere queryable removes most of the manual effort.
- 02
Automated extracts
Data lands somewhere central on a schedule, but transformations are ad hoc and definitions live in people's heads.
Next stepModel it. Agree the definitions, build tested transformations, and make one table the answer.
- 03
Modelled warehouse
Consistent definitions, tested pipelines, dashboards that reconcile with finance.
Next stepBroaden access and add alerting, so data pushes to people rather than waiting to be looked at.
- 04
Data as a product
Teams self-serve, data quality is monitored, and models feed back into the applications themselves.
Next stepPredictive work and AI over your own data become genuinely viable — not before.
We'll tell you which level you're at in the first session, and it's frequently one lower than expected.
Six services covering the path from scattered sources to decisions.
Data pipelines
Reliable extract-and-load from your ERP, CRM, application databases, payment providers and ad platforms — with retries, alerting and no silent failures.
Warehouse design
A modelled warehouse where a customer means one thing. Includes the boring, decisive work of writing down definitions everyone signs off.
Transformation & testing
Version-controlled, tested transformations, so a change to how revenue is calculated is reviewable rather than a spreadsheet formula somebody edited.
Dashboards & reporting
Dashboards built around the decisions they support rather than every metric available. Fewer charts, each of which someone acts on.
Alerting & anomaly detection
Push, not pull — get told when a number moves unexpectedly instead of finding out when someone opens a report next Tuesday.
Data quality monitoring
Automated checks on freshness, volume, nulls and referential integrity, so you learn about a broken feed from a monitor rather than from a board meeting.
Deliberately conventional. Data platforms outlive the teams that build them, so novelty is a liability here more than anywhere.
Ingestion
Scheduled and event-driven loads from your operational systems, landing raw and unmodified so you can always reprocess history.
- Airflow
- Custom connectors
- CDC
- Webhooks
Storage
A warehouse sized to your actual data volume. Most mid-market companies need far less infrastructure than they're sold.
- PostgreSQL
- ClickHouse
- BigQuery
- Snowflake
Transformation
SQL transformations in version control, tested and documented, producing the modelled tables everything else reads from.
- dbt
- SQL
- Python
- CI checks
Consumption
Dashboards, scheduled reports, alerts and APIs — all reading the same modelled layer, so they cannot disagree.
- Metabase
- Power BI
- Looker Studio
- REST APIs
Four causes, all preventable, and each one is why a reporting project quietly fails.
Definitions were never agreed
Sales counts an order at purchase, finance at delivery, ops at dispatch. All three are right and all three disagree. We force this conversation early and write the answer down.
Failures are silent
A feed breaks, the dashboard keeps rendering yesterday's data, and nobody notices for a fortnight. Freshness monitoring is not optional.
Nothing reconciles
If the warehouse revenue figure doesn't tie to the accounting system to the rupee, people will use the accounting system. We build reconciliation checks in.
Too many charts
A dashboard with forty metrics gets ignored. One with the six numbers that change a decision gets opened daily. We design for the decision, not for completeness.
If you have one application database and modest reporting needs, querying a read replica may be entirely sufficient — and we'll say so rather than sell you a warehouse. You need one when data lives in several systems that must be joined, when reporting queries are slowing the production database, or when you need history the operational system overwrites.
Start a project
Name the report someone rebuilds by hand every month. That's usually the right place to start, and the easiest to justify.
- Reply within one business day
- Free scoping session, no obligation
- You keep the scope document either way