Data engineering & analytics
One version of the truth, that people actually trust
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.
We'll assess your current reporting stack free in the first session.
What data and analytics work involves
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 Indian mid-market starting point is a set of exports into spreadsheets, reconciled by hand each month by someone senior enough that their time is expensive. That's the problem worth solving first.
Where are you now?
Four stages. Knowing which one you're at determines what's worth doing next — and skipping a stage rarely works.
Spreadsheet reporting
Looks like: Monthly exports, manual joins, one person who knows how it works, numbers that don't always agree.
Next: Automate the extract. Even landing raw data somewhere queryable removes most of the manual effort.
Automated extracts
Looks like: Data lands somewhere central on a schedule, but transformations are ad hoc and definitions live in people's heads.
Next: Model it. Agree the definitions, build tested transformations, and make one table the answer.
Modelled warehouse
Looks like: Consistent definitions, tested pipelines, dashboards that reconcile with finance.
Next: Broaden access and add alerting, so data pushes to people rather than waiting to be looked at.
Data as a product
Looks like: Teams self-serve, data quality is monitored, and models feed back into the applications themselves.
Next: Predictive 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.
What we build
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.
How we build it
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
Why people stop trusting dashboards
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.
FAQ
Data & analytics questions
Do we need a data warehouse, or is our database enough?
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.
Which BI tool should we use?
It matters far less than the modelling underneath. Metabase is excellent value and easy for non-technical users; Power BI makes sense if you're already in the Microsoft ecosystem; Looker Studio is fine for lighter needs. We build the modelled layer so the tool stays swappable, because you will change it eventually.
How long before we see something useful?
A first working pipeline and a dashboard on one genuinely important question is typically four to six weeks. We deliberately start with one question that matters rather than building the whole warehouse first — it proves the approach and usually reshapes the roadmap.
What about data privacy and residency?
Handled as part of the design. Under DPDP that means being explicit about what personal data is stored, where, for how long and who can query it. We keep personal data out of the analytical layer wherever the analysis doesn't genuinely need it — pseudonymised by default.
Can you work with our existing data team?
Frequently. Common shapes are building the platform for an analyst team that can then self-serve, or adding data engineering capacity to a team strong on analysis but short on pipeline skills. We're comfortable being the layer underneath rather than owning everything.
Is this a prerequisite for AI work?
For most useful AI, yes — and we'd rather say so before selling you an AI project. Retrieval quality depends on well-organised source material, and predictive work depends on consistent historical data. If your data is a mess, fixing it is the higher-return project and should come first.
Related services
What question can't you answer today?
Name the report someone rebuilds by hand every month. That's usually the right place to start, and the easiest to justify.